{
 "cells": [
  {
   "cell_type": "markdown",
   "metadata": {
    "slideshow": {
     "slide_type": "slide"
    }
   },
   "source": [
    "# Normal Distribution\n",
    "\n",
    "- Different displays of normally distributed data\n",
    "- Compare different samples from a normal distribution\n",
    "- Check for normality\n",
    "- Work with the cumulative distribution function (CDF)\n",
    "\n",
    "Author:  Thomas Haslwanter, Feb-2017"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 1,
   "metadata": {
    "collapsed": false,
    "slideshow": {
     "slide_type": "fragment"
    }
   },
   "outputs": [
    {
     "name": "stdout",
     "output_type": "stream",
     "text": [
      "Populating the interactive namespace from numpy and matplotlib\n"
     ]
    }
   ],
   "source": [
    "%pylab inline\n",
    "import scipy.stats as stats\n",
    "\n",
    "# seaborn is a package for the visualization of statistical data\n",
    "import seaborn as sns\n",
    "sns.set(style='ticks')"
   ]
  },
  {
   "cell_type": "markdown",
   "metadata": {
    "slideshow": {
     "slide_type": "slide"
    }
   },
   "source": [
    "## Different Representations"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 2,
   "metadata": {
    "collapsed": false,
    "slideshow": {
     "slide_type": "fragment"
    }
   },
   "outputs": [
    {
     "data": {
      "image/png": "iVBORw0KGgoAAAANSUhEUgAAAjoAAAGACAYAAACk8chOAAAABHNCSVQICAgIfAhkiAAAAAlwSFlz\nAAALEgAACxIB0t1+/AAAIABJREFUeJzs3Xd8Tff/wPHXHdkDiT0So4IgZhFipmoUsVIJDVWl1daX\nWtVSRRVt9attFFW/qq+vVbvot8sWakRDhNhCEMRI3Ky7zu8Pza1LgkTkZryfj9665/M5431O7vnc\n9z3ro1IURUEIIYQQoghS2zoAIYQQQohnRRIdIYQQQhRZkugIIYQQosiSREcIIYQQRZYkOkIIIYQo\nsiTREUIIIUSRpbV1AEIIa/Hx8QQGBjJ9+nSCg4Mt5f/3f//H6dOnmTVrVr7Gs27dOn799Ve+/fZb\nq/L9+/czdOhQqlWrBoDZbKZUqVK8+eabtGzZEoCJEyfy0ksvWYazMmnSJEJCQqhXr95DdZnTe3l5\n0b17d/76668cxb5jxw6OHDnCyJEj2bp1K/v27WPSpEk5mkduTJgwgYiICDw8PFCpVBiNRqpUqcL0\n6dPx9PQkLCyMy5cv4+bmBoDRaKRt27a89dZbuLq6AlCrVi18fHxQq//5PVqvXj0++eSTZx6/EEWJ\nJDpCFEBqtZpPP/2Upk2bWhKJgsjLy4uNGzdahmNjYxkyZAjz5s2jQYMGT/SlvHfvXvr165dlXeb0\n8fHxuYovOjqapKQkAAIDAwkMDMzVfHLj1VdfZciQIZbhWbNmMXXqVL7++msAxo8fT+fOnQEwGAxM\nnz6dsWPHsmDBAss0S5YswcPDI99iFqIokkRHiALI0dGRwYMHM2bMGFauXIm9vb1V/d27d5k6dSqx\nsbGoVCpat27N6NGj0Wq11KtXj8DAQGJjY5k9ezb9+/dn8ODB7N27l9TUVN555x1++eUXTp06Rdmy\nZVmwYAHOzs6sWbOGVatWYTAYSEpKYujQofTv3z9HcdeuXZuwsDB++OEH5syZQ1hYGAMGDOCFF17g\n448/5vDhw9jZ2VG5cmVmzpzJwoULuX79OmPHjuWzzz5j9uzZlChRgnPnzhEaGspvv/3GgAEDqFev\nHmazmYkTJxITE4NWq2XSpEk0bNiQ8PBwbt++zeTJkwEsw0FBQaxcuRKTyYSbmxve3t6WI1MJCQlM\nmTKFy5cvoygKPXv25PXXXyc+Pp5XX32Vtm3bcuTIEZKSkhg/fjwdO3Z86r+pv78/n3/+eZZ1dnZ2\nvP/++7Rq1YqzZ89So0aNp16eEOIeuUZHiAJq+PDhODk5MWfOnIfqpk+fTsmSJdm0aRNr167l5MmT\nfP/998C9owPt27fn119/pX79+uj1ekqXLs2aNWvo2bMnkyZNYuLEifz888/odDq2bt1KSkoKq1ev\nZuHChWzYsIE5c+Zk+6X8OLVr1+bUqVNWZVFRURw4cICffvqJdevWUaVKFU6ePMm7775L2bJlmT17\nNg0aNADA3d2dn3/+mbCwMKt5pKen06pVKzZs2MDIkSMZNWoUer0+2zgaNGhASEgIXbt25d1337Wq\nGzt2LM2bN2fTpk2sWLGCn376iS1btgBw6dIlAgICWLNmDWPHjmXGjBm52g4Pxr5hwwaaN2+e7TiO\njo5UrVrVatsNGjSIoKAgy+vmzZtPHYsQxY0c0RGigFKr1Xz++ef06tWLgIAAq7pdu3axYsUKVCoV\n9vb2hISEsGTJEoYNGwZA06ZNrcbv1KkTcO9Uk4+PD+XKlQOgcuXKJCUl4eLiwoIFC9i5cycXLlwg\nNjaW1NTUXMWtUqlwdHS0KvPx8UGj0RAcHExAQACdOnXCz88vy+kfjD2Tu7s7Xbt2BaB169YoisK5\nc+dyHF9qaiqHDx+2JIZubm707t2bXbt20aBBA+zs7Gjbti0Avr6+3LlzJ8fLAPjhhx/46aefADCZ\nTDz//POMHj36kdOoVCqcnJwsw3LqSoinJ4mOEAVYxYoVmTJlCu+99x49e/a0lJvNZqvxzGYzRqPR\nMuzs7GxVb2dnl+X7TAkJCfTr14+XX36ZJk2a0LlzZ7Zv356rmKOjo/Hx8bEqc3d3Z+PGjRw+fJg/\n//yTUaNGMWTIEAYMGPDQ9A/Gnun+i3IBFEXBzs4OlUrF/V32GQyGR8ZnNpt5sIu/+7efnZ2dZVkq\nlSrLeaxYsYKVK1cC2V8g/OA1Oo+TlpbG2bNnqVmz5hNPI4R4PDl1JUQB16VLF9q0acOSJUssZQEB\nASxbtgxFUdDr9fz444+PvLPpcY4dO4aHhwdvvfUWAQEBliTHZDLlaD5Hjx5lxYoVDBo0yKp8+/bt\nvPrqqzRq1IgRI0bQs2dPjh07BoBGo7FK0rJz584dS1zbtm3DwcEBb29vSpUqRUxMDIqioNPprBK0\nrObt6upKgwYNWLZsGXDveqcNGzbkaPuFhoayceNGNm7cmCd3QaWnpzNjxgzatGlDpUqVnnp+Qoh/\nyBEdIQqBSZMmERkZaTU8ffp0unfvjsFgoHXr1rz55pu5nn+rVq1Ys2YNnTt3RqVS0axZMzw8PIiL\ni3vkdBcvXiQoKAi4d8TF1dWV2bNnU7t2bavx2rRpw65du+jWrRvOzs6UKFGCjz/+GICOHTsybtw4\npkyZ8shleXp68ttvv/Hll1/i5OREeHg4Wq2WHj16sHv3bl588UXKlStHs2bNLEds/P39GTNmDB9/\n/DF169a1zGv27NlMmzaNdevWodfr6d69O7179+by5cs53XS59tlnnzF//nzUajVGo5GWLVsyceLE\nfFu+EMWFSnnwGK4QQgghRBEhp66EEEIIUWRJoiOEEEKIIksSHSGEEEIUWZLoCCGEEKLIKtB3XaWn\np3Ps2DHKlCmDRqOxdThCCCGEyCcmk4kbN25Qr169hx5CmhO5SnTMZjNTpkzh5MmT2NvbM336dLy9\nvR8aZ9iwYQQGBhIaGpqr4I4dO5blA8WEEEIIUTwsW7Ys2yemP4lcJTp//PEHer2eVatWERUVxaxZ\ns5g/f77VOF9++SXJycm5DgygTJkywL2VLF++/FPNSwghhBCFR0JCAgMGDLDkArmVq0QnMjKS1q1b\nA9CwYUPLE04z/fLLL5YelZ9G5umq8uXLU7ly5aealxCicNCl6jl58TZXE1O4disVV2c7Knq64l3B\nDa/y7rYOTwiRz5720pVcJTo6nQ5XV1erIIxGI1qtllOnTrF582a+/vprvvnmmyeeZ3h4OHPnzs1N\nOEKIIuD0pdv8HHGBXVGX0Ruy7nqiZpWSdG1ZjdaNKuFgJ9ftCSEeL1eJjqurKykpKZZhs9mMVntv\nVhs2bODatWsMGjSIy5cvY2dnR6VKlWjTps0j5zlixAhGjBhhVRYfH09gYGBuQhRCFBIpaQa+2xjN\n1oOXAKjg6UKbxpWoUtaNch7O6NIMXEnUceRUIodOJPDVqr/48Y9TjAxpRN3qnjaOXghR0OUq0Wnc\nuDHbt2+na9euREVFWfVUPH78eMv78PBwSpcu/dgkRwhRPB05dYMvVx4mMSmdGpVLMLCLLw19yqBW\nP9hreDl6tK7B9dupbNx5lk17zvH+vD0EtanBwK6+2GnlSRlCiKzlKtHp2LEjERERhISEoCgKM2bM\nYPHixXh5eckRGCHEE9l68CJf/xiFCujfqTbBgTXRah6dsJQt5czQnvUJaFCJL1ceZsPOs8RdTeb9\nV5vh5FCgn5YhhLCRAt2pZ+apq61bt8rFyEIUIet3nOH7TTG4Otnx4ZDm+FbL+Smo9Awjny49xKET\n16jlVYrJr7fA3cX+GUQrhLCFvMoB5HivECJfrd12mu83xeBZwpFZ7wTkKskBcHTQMnFwMzo0rcLJ\ni7eZOD8CXZohj6MVQhR2kugIIfLNjshL/LDlOKVLOvHZO63xfsrbxbUaNSP7NaJLy6pcuJrMzB8O\nYDCa8yhaIURRIImOECJfHDl9g69W/YWLo5YpQ1tQ1sM5T+arVqt4o5cf/vUrcPRMIl+v+guzucCe\nkRdC5DNJdIQQz9zVxBRm/nAAUDFxcPOnPpLzII1axZgBTajtXYodh+NZvfVUns5fCFF4SaIjhHim\n9AYTs/5zkJR0I+8EN6D+c6WfyXIc7DRMeq05ZUo5sfzXWI6cvvFMliOEKFwk0RFCPFPfbTzGuctJ\nvNjcm8DnvZ7pskq4OvBeWFPUahWz/xvJzaS0Z7o8IUTBJ4mOEOKZ2Xk4nl/2XaBaRXeG9aqfL8us\n5e3B4G51uaPL4PP/RmKS63WEKNYk0RFCPBOJd9KYv/YITg4aJgx8Pl/7pureujr+9SsQc+4mG3ee\nzbflCiEKHkl0hBB5TlEUvl71FynpRob0qEfFMq6PnygPqVQq3u7bgJJuDiz93wniEpLzdflCiIJD\nEh0hRJ77Zd8F/jp1gya1y/Jic2+bxFDC1YERwQ0xmszMWXEYo0meryNEcSSJjhAiTyXcTLF07zDi\n5YaoVA920Jl/mtUtzwvPe3E2PonVW0/bLA4hhO1IoiOEyDOKojB/7VHS9SaG9aqPZwknW4fE0J71\n8HB3ZPXWU1y5obN1OEKIfCaJjhAiz+z86zKHT16nca2ytGtcMDridXa0Y1iv+hiMZuatPUIB7sdY\nCPEMSKIjhMgTySl6Fm2Mxt5Ow/A+fjY9ZfWglvUr0LROOY6cTmTn4XhbhyOEyEeS6Agh8sQPm2NI\n0ukZ0Kk25T1dbB2OFZVKxZu9/bC30/B/P8WgS9XbOiQhRD6RREcI8dRiL9zi9wMXqVrBnaA21W0d\nTpbKeTgT+mIt7ugy+GHLcVuHI4TIJ5LoCCGeismsMH/dUQDe7O2HRlNwm5WebWvgVd6NX/+M48T5\nW7YORwiRDwpuiySEKBR+/fMC5y4n0b5JZepW97R1OI+k1ah5u28DAOatPSLP1hGiGJBERwiRa0m6\nDJb+fAJnRy2Du9W1dThPxLeaJy829+bC1WR+2iXdQwhR1EmiI4TItf/8fAJdmoH+nWpTyt3R1uE8\nsVe7+eLuYs+K306SeEd6OBeiKJNERwiRK6cu3ub3A3F4l3ejW6tqtg4nR9yc7Rn0ki/pehPfb4qx\ndThCiGdIEh0hRI5lXoCsKAX/AuTsvPC8F7W8SrE76jJHz9ywdThCiGek8LVOQgib+31/HGcu3aFt\no8rUq1Ha1uHkilqt4o3e9VGpYMG6aLkwWYgiShIdIUSOJKfo+c/Px3Fy0DC4u6+tw3kqNauU4sXm\n3ly6dpfNe87ZOhwhxDMgiY4QIkeW/u8Ed1MNhL5Yu0B02vm0Bnb1xc3ZjuW/nuRWcrqtwxFC5DFJ\ndIQQT+z0pdv8+ucFqpRzo3vrgvkE5Jxyd7EnrKsvaRlGFsuFyUIUOZLoCCGeiNms8O266L8vQK6P\nthBegJydF5t781zlEuw4HM+xs4m2DkcIkYeKTkslhHimfj9wkZMXb9O6YSX8nitj63DylEZ9r9NP\ngG/XR2OSC5OFKDJyleiYzWYmT55Mv379CAsLIy4uzqr+hx9+IDg4mODgYObOnZsngQohbCdJl8GS\nLTE4OWgY0qNwPAE5p2p5e9CxmRcXriazJeK8rcMRQuSRXCU6f/zxB3q9nlWrVjFmzBhmzZplqbt0\n6RI//fQTK1eu5Mcff2TPnj3ExsbmWcBCiPy3eHMMd1MNDOhcp0hcgJydQS/54uJkx7JfY7l9Vy5M\nFqIoyFWiExkZSevWrQFo2LAhx44ds9SVL1+eRYsWodFoUKlUGI1GHBwc8iZaIUS+iz6byNaDl6he\nsUShewJyTpVwdSCsSx1S0438sPm4rcMRQuQBbW4m0ul0uLq6WoY1Gg1GoxGtVoudnR0eHh4oisJn\nn32Gr68v1ao9vnEMDw+X01xCFDAGo5n5a4+gUsFbfQvnE5BzqrN/VX77M45thy7xYnPvAt8juxDi\n0XLVarm6upKSkmIZNpvNaLX/5EwZGRmMHTuWlJQUPvrooyea54gRIzh58qTVa+vWrbkJTwiRRzbs\nPMOlazo6+1ellreHrcPJFxq1iuF97l2YPG/tEQxGuTBZiMIsV4lO48aN2bVrFwBRUVH4+PhY6hRF\n4a233qJWrVpMmzYNjUaTN5EKIfJVws0UVv5+ipKuDgzsWrifgJxTtat60Nm/KhcT7rJ+xxlbhyOE\neAq5OnXVsWNHIiIiCAkJQVEUZsyYweLFi/Hy8sJsNnPgwAH0ej27d+8GYPTo0TRq1ChPAxdCPDuK\novDt+mj0BhMjghvg6mRn65Dy3aCXfNl/7Cqrfj9JQMOKVCzt+viJhBAFTq4SHbVazbRp06zKatSo\nYXkfHR39dFEJIWxqX/RVDp24ht9zpWnbuLKtw7EJVyc7hvasz2dLDzF/zVGmveGPSqWydVhCiBwq\n+lcWCiFy5G6qnvnrjmKnVTO8j1+x/nIPaFCRpnXKEXX6Br8fuGjrcIQQuSCJjhDCysIN0dy5m0H/\nTrWpXNbN1uHYlEql4q0+DXB21PJ/Px0j8U6arUMSQuSQJDpCCIsDxxPYERlPzSol6dW2xuMnKAbK\nlHLite71SE03Mnd1FIqi2DokIUQOSKIjhADunbL6ZvURtBoVI/s1KhbPzHlSLzb3oqFPGSJjr7P1\noJzCEqIwkZZMCIGiKHyz+gi3ktMJfbE23hXcbR1SgaJSqRgR3BBnRy0LN0RzNTHl8RMJIQoESXSE\nEGw9eImIo1fwreZBnw41bR1OgVTWw5k3e/uRlmHi38sjpYdzIQoJSXSEKOauJqawcMNRnB21jO7f\nBI26+N5l9TjtGlemTcNKxMbd5sc/Ttk6HCHEE5BER4hizGA08fl/D5GWYWJ4bz/KeTjbOqQCTaVS\nMbxvA8qUcmLlH6eIPpto65CEEI8hiY4Qxdiijcc4fekOHZpWoV2TKrYOp1BwdbJj7IAmAHy+9BC3\nktNtHJEQ4lEk0RGimNoReYmf916gagV3SyeW4sn4VvNkcDdfbt/N4LOlh+R6HSEKMEl0hCiGzl9J\nYu6aIzg5aJkw6Hkc7XPVG0yxFtSmBi39KhBz7iaLNx+3dThCiGxIoiNEMXMrOZ1pi/4kQ2/i3dBG\nVCojnVXmhkp173lDlcu6snHXWX7984KtQxJCZEESHSGKkXS9kY+/309iUjoDu9bBv35FW4dUqDk7\n2jF5SAvcnO2Zv/YoR07dsHVIQogHSKIjRDFhMpn59/LDnLl0h8Dnq9BXnpeTJyqUdmHi4GaoVCpm\n/ucgcVeTbR2SEOI+kugIUQyYzQpf/xjFvuir+D1Xmrf7NizWvZLntbrVPRnZryEpaQYmfbuXKzd0\ntg5JCPE3SXSEKOIUReHb9UfZdugSPl4lmTi4GXZa2fXzWrsmVXijV33u3M1g4oK9XL+dauuQhBBI\noiNEkWY2KyxcH225jXzKUH+cHe1sHVaR1S2gOgO71iHxThofzIsg4ab0iSWErUmiI0QRZTSZmbPy\nMJsjzuNd3o1pb/jj5mxv67CKvOBAH/q/WItrt1J5b+5uuWZHCBuTREeIIig13cAniw+wIzKeWt6l\nmPl2AKXcHG0dVrER2qk2Q4PqcSs5gwnf7OGYdBUhhM1IoiNEEXMlUcfYr3dz6MQ1GvmUYfobLeVI\njg30aFODUSGNSMswMmnBXv6397ytQxKiWJLHoQpRhByISeDfKw6TkmagR5vqvNatLhqN/J6xlcDn\nvSjr4cysJQeZt/YoZ+KTGNqznjyJWoh8JC2gEEVAWoaRuauj+Pj7/egNJkb2a8TQoPqS5BQA9WuU\nZs6otlSvWILf9scx6t87OHXxtq3DEqLYkFZQiEIuMvYaI7/Ywa9/xlG1gjtzRrXlhWZetg5L3Kes\nhzOf/6s1PdvW4PKNFMaF7+aHzTGkZRhtHZoQRZ4cPxWikLqSqOP7n2LYH5OAWq2id7vneKVLbey0\nGluHJrJgb6dhSI96NK1Tjq9X/cXa7WfYcTiewd3q0rphJdRqeYCjEM+CJDpCFDJXEnX8+McptkfG\nYzYr1K3uyRu96lOtYglbhyaeQIOaZfhmfAfWbDvNuu1nmL0sktVbTxH6Ym3861eQhEeIPCaJjhCF\ngNmsEHXqBj/vPc/B4wmYFahSzo3+nWrRyq+idOdQyDjaa3mlcx1eeN6LFb+dZEfkJWb95yAVPF3o\n0rIqgc974e4id8oJkRck0RGigDKbFc7E32F31GX2HLlC4p00AJ6rUpLebZ+jVYOK8uu/kCvv6cK7\noY3p94IPa7adZufheL7fFMN/fj5Bk9plad2wEs/7lpOnWQvxFCTREaKAUBSFyzd0xF64zZEzN4g6\neYM7ugwAnB21vPC8F11aVsXHq5SNIxV5rWIZV/7VrxGDu9dl68GL/H7gIvtjEtgfk4BGraJ2VQ8a\n1SqDbzVPalYuiaODNN1CPKlc7y1ms5kpU6Zw8uRJ7O3tmT59Ot7e3pb6H3/8kZUrV6LVahk+fDjt\n27fPk4CFKOwMRjM3k9KIv64j/vrdv//VEXc1GV2awTJeKTcHOjStgn/9CjSuVRZ7O7nIuKhzc7an\nZ9vn6Nn2OeISktkTdYXI2GscP3+TmHM3AVCrVVQp60rlcm5ULutK5TKuVC7rRoXSLjg7auU0phAP\nyHWi88cff6DX61m1ahVRUVHMmjWL+fPnA3Djxg2WLl3K2rVrycjIoH///rRq1Qp7+9ydczabFUwm\ns2VYyWY8JbuKbKbIbvzs5//kE+R4HtmO/8SLfOQEWZVmO+88izFnwefFNst+nbJbZs6CMSsKBqMZ\no8mMwXjvZTSaMfw9nGEwkZZuIDXdSGqGkdR0A7pUA7fvpnP7bga3kzO4m6p/aL5q1b3TGE3rlKO2\ndyl8q3tStYK7fGkVY97l3fHu7M6AzrVJ0mVw7OxNYuNucTLuNheuJhGXcPehaeztNJRyc7j3cnfE\n3cUeJwctTg5aHO21ODlqcbLX4Oigxclei52dGq1GjVqtQpP50qj/fq9Go7lXplarsHwSVf+8f/Dj\nqVLdP94/ZfcN/j2Nymp6+ZSLB+XlM8BynehERkbSunVrABo2bMixY8csdUePHqVRo0bY29tjb2+P\nl5cXsbGx+Pn55WpZw2b+gZ2zR25DFaJAcHWyo5S7A9UquuPh7njv13jZe7/KK5ZxkdvCRbZKuDrQ\nqkFFWjWoCNxL/m8lpxN/7Z+jggm3Urnzd0J9+tIdTOac/WARoiDp0bo6XZrmzWn6XCc6Op0OV1dX\ny7BGo8FoNKLVatHpdLi5uVnqXFxc0Ol0j5xfeHg4c+fOzbKuTlUPXEqUtipTZfcbIGfF2f9izqv5\nZDludvPOuiKnP+qznf8zjDGvtle2EWZRkePtlUcx2mnVf780aDUq7LQaS5m9nQYXRy3OjnY4OWhx\ndtTi4mRHKTcHSWREnlGpVHiWcMKzhBMNfMo8VG82K9xN1ZOcoictw0i63kh6hsnyPi3DSFq6EYPJ\njMmkYDIrmMzme/+a/nlvvq8OrI+MZr7PPCpqXfd3maXgn/KHyrI/Pi2KsZpVSubZvHKd6Li6upKS\nkmIZNpvNaLXaLOtSUlKsEp+sjBgxghEjRliVxcfHExgYyJgBTahcuXJuQxVCiGJFrVZRwtWBEq4O\ntg5FiFyLj4/Pk/nkOtFp3Lgx27dvp2vXrkRFReHj42Op8/Pz48svvyQjIwO9Xs/Zs2et6p+UyWQC\nICEhIbdhCiGEEKIQyvzuz8wFcivXiU7Hjh2JiIggJCQERVGYMWMGixcvxsvLi8DAQMLCwujfvz+K\novDuu+/i4JDzXxY3btwAYMCAAbkNUwghhBCF2I0bN6zu6s4plZLTW2zyUXp6Og0aNOC3335Doyme\n1zcEBgaydetWW4dhM8V9/UG2gay/rL+sf/Fcf5PJxIsvvsiRI0dwdHTM9XwK9FOnMlfsaTK5oqC4\nX59U3NcfZBvI+sv6F2fFff2fJskByLsb1YUQQgghChhJdIQQQghRZEmiI4QQQogiSzNlypQptg7i\ncZo3b27rEGxK1r94rz/INpD1l/UvzmT9n279C/RdV0IIIYQQT0NOXQkhhBCiyJJERwghhBBFliQ6\nQgghhCiyJNERQgghRJEliY4QQgghiixJdIQQQghRZBWovq5+//13fvnlF7744gsAoqKi+OSTT9Bo\nNAQEBPDOO+9YjX/r1i3Gjh1Leno6ZcuWZebMmTg5Odki9DyzcOFCdu/eDUBycjKJiYlERERYjTN8\n+HBu376NnZ0dDg4OLFq0yBahPhOKotCmTRuqVq0KQMOGDRkzZozVOHPnzmXHjh1otVo++OAD/Pz8\nbBDps3H37l3GjRuHTqfDYDAwYcIEGjVqZDXO9OnTOXz4MC4uLgDMmzcPNzc3W4SbZ8xmM1OmTOHk\nyZPY29szffp0qz7ufvzxR1auXIlWq2X48OG0b9/ehtHmPYPBwAcffMDly5fR6/UMHz6cwMBAS/0P\nP/zA6tWr8fDwAGDq1KlUr17dVuE+M7169cLV1RW417/TzJkzLXVF/TOwbt061q9fD0BGRgYnTpwg\nIiICd3d3oGju95mOHDnC7NmzWbp0KXFxcUyYMAGVSkXNmjX56KOPUKv/OSaTnp7OuHHjuHnzJi4u\nLnz66aeW/SJbSgHx8ccfK506dVJGjRplKevRo4cSFxenmM1m5fXXX1diYmIemmbt2rWKoijKt99+\nqyxevDg/Q37mhg0bpuzevfuh8i5duihms9kGET17Fy5cUN54441s648dO6aEhYUpZrNZuXz5stK7\nd+98jO7Z++qrryyf47Nnzyo9e/Z8aJyQkBDl5s2b+RzZs/Xrr78q7733nqIoivLXX38pb775pqXu\n+vXrSrdu3ZSMjAwlOTnZ8r4oWbNmjTJ9+nRFURTl9u3bStu2ba3qx4wZo0RHR9sgsvyTnp6uBAUF\nZVlXHD4D95syZYqycuVKq7KiuN8riqIsXLhQ6datmxIcHKwoiqK88cYbyp9//qkoiqJ8+OGHym+/\n/WY1/vfff698/fXXiqIoyubNm5WPP/74scsoMKeuGjduzP0PadbpdOj1ery8vFCpVAQEBLB3716r\naSIjI2ndujUAbdq0eai+MPvtt99wd3cnICDAqjwxMZHk5GTefPNNQkND2b59u40ifDZiYmK4du0a\nYWFhDB2gOVddAAAgAElEQVQ6lHPnzlnVR0ZGEhAQgEqlomLFiphMJm7dumWjaPPeq6++SkhICAAm\nkwkHBwererPZTFxcHJMnTyYkJIQ1a9bYIsw8d/++3LBhQ44dO2apO3r0KI0aNcLe3h43Nze8vLyI\njY21VajPROfOnRk5ciRw76imRqOxqo+JiWHhwoWEhoby7bff2iLEZy42Npa0tDRee+01Bg4cSFRU\nlKWuOHwGMkVHR3PmzBn69etnKSuq+z2Al5cX4eHhluGYmBiaNWsGZP29/uD3/r59+x67jHw/dbV6\n9WqWLFliVTZjxgy6du3K/v37LWU6nc5yCBPAxcWFS5cuWU2n0+ksh+5cXFy4e/fuM4w872W3Lfz8\n/Pj222/597///dA0BoPB0hAkJSURGhqKn58fnp6e+RV2nslq/SdPnsywYcPo0qULhw4dYty4caxd\nu9ZSr9PpKFmypGU48+/+2EOXBdCj/v43btxg3LhxfPDBB1b1qampvPLKKwwePBiTycTAgQOpV68e\ntWvXzs/Q89yD+7tGo8FoNKLVaq32c7j3N9fpdLYI85nJPB2h0+n417/+xahRo6zqX3rpJfr374+r\nqyvvvPMO27dvL3KnbhwdHRkyZAjBwcFcuHCBoUOH8ssvvxSbz0Cmb7/9lrffftuqrKju9wCdOnUi\nPj7eMqwoCiqVCsj6ez033/v5nugEBwcTHBz82PFcXV1JSUmxDKekpFjOVT44jqOjY5b1BV122+LM\nmTO4u7tbXaOQqXTp0oSEhKDVavH09KROnTqcP3++UCY6Wa1/Wlqa5dds06ZNuX79utUHP6vPRWE9\nT53d3//kyZOMHj2a8ePHW37ZZHJycmLgwIGWa9FatGhBbGxsoW/wHvy7ms1mtFptlnWF+W/+KFev\nXuXtt9+mf//+dO/e3VKuKAqDBg2yrHPbtm05fvx4kUt0qlWrhre3NyqVimrVqlGyZElu3LhBhQoV\nis1nIDk5mfPnz9OiRQur8qK632fl/utxHvW9n119lvPM2xDzjqurK3Z2dly8eBFFUdizZw9Nmza1\nGqdx48bs3LkTgF27dtGkSRNbhJrn9u7dS5s2bbKtyzzEnZKSwunTp4vURYlz5861HOWIjY2lQoUK\nliQH7v3N9+zZg9ls5sqVK5jN5kJ5NCc7Z86cYeTIkXzxxRe0bdv2ofoLFy4QGhqKyWTCYDBw+PBh\n6tata4NI81bjxo3ZtWsXcO8mBB8fH0udn58fkZGRZGRkcPfuXc6ePWtVXxQkJiby2muvMW7cOPr2\n7WtVp9Pp6NatGykpKSiKwv79+6lXr56NIn121qxZw6xZswC4du0aOp2OMmXKAMXjMwBw8OBB/P39\nHyovqvt9Vnx9fS1nd3bt2pUn3/sF6q6rB02dOpWxY8diMpkICAigQYMG3Llzh0mTJjF37lyGDx/O\ne++9x48//kipUqUsd2sVdufPn6dVq1ZWZZ999hmdO3embdu27Nmzh5dffhm1Ws3o0aOL1Bf9sGHD\nGDduHDt37kSj0Vjuushcfz8/P5o2bUq/fv0wm81MnjzZxhHnrS+++AK9Xs8nn3wC3Ev458+fz+LF\ni/Hy8iIwMJCgoCBefvll7OzsCAoKombNmjaO+ul17NiRiIgIQkJCUBSFGTNmWK1zWFgY/fv3R1EU\n3n333YeuXSrsFixYQHJyMvPmzWPevHnAvSN+aWlp9OvXj3fffZeBAwdib2+Pv79/lklwYde3b1/e\nf/99QkNDUalUzJgxg6VLlxabzwDca/srV65sGS7q+31W3nvvPT788EP+/e9/U716dTp16gTAa6+9\nxoIFCwgNDeW9994jNDQUOzu7J/rel97LhRBCCFFkFdhTV0IIIYQQT0sSHSGEEEIUWZLoCCGEEKLI\nkkRHCCGEEEWWJDpCCCGEKLIk0RFCCCFEkSWJjhBCCCGKLEl0hBBCCFFkSaIjhBBCiCJLEh0hhBBC\nFFmS6AghhBCiyJJERwghhBBFliQ6QgghhCiyJNERQgghRJEliU4OmEwmFi9eTO/evQkKCqJr1658\n/vnn6PV6AMLDw2nRogVBQUEEBQXx0ksvMXr0aC5cuGCZR1hYGB06dLCMk/kqiMLDw5k2bdpjx3vt\ntde4desWAEOHDuXMmTN5Hkt0dDQdOnQAYMWKFSxcuPCR469evZply5ZlWXf/9B06dCA6OjpHsVy6\ndIkRI0YAcO3aNUJCQnI0/aPs2bOH9u3b06dPH9LT0x+7fCFsISoqirCwMLp37063bt14/fXXOX36\ntKW+Vq1adO/e3aqNmzhxos3ifZI2Izv79++nW7duWdZ99NFHdOjQgTlz5mQ7/aPaIpE/tLYOoDCZ\nMmUKSUlJLFmyBDc3N1JTUxk7diwTJ07k888/B6Br165MnjzZMs2GDRsYNGgQW7ZswdXVFYDx48fT\nuXNnm6zDsxAREWF5/9133z3z5YWGhj52nMjISGrWrJnr6R/lypUrnD9/HoBy5cqxcuXKp5rf/bZs\n2UJwcDBvvfXWEy1fiPym1+t54403+P7776lbty4AGzduZOjQoWzduhWNRgPAkiVL8PDwsGWoFk+7\nz2dn1apV7Nixg/Lly2c7zqPaIpE/5IjOE7p06RKbNm1ixowZuLm5AeDs7MzUqVPp2LFjttP17NmT\nGjVqsGnTpscuY+vWrQwdOjTLuiNHjhAcHEy3bt3o1asX+/btA+79cso8mnL/8P79++nXrx+jRo0i\nKCiIkJAQtm3bxuDBg2nXrh0zZswAHv61kt2vl+3btxMSEkLv3r1p164dX375JQDvv/8+AIMGDeLq\n1auWIyRjxozh//7v/yzTr1ixglGjRgGwbds2goOD6dmzJyEhIfz1119ZrvPy5cvp1KkTffr0Yfny\n5Zby+480LV++nB49etCnTx/69+/PmTNn+P3339m2bRs//PADy5YtIzw8nCFDhtC9e3fGjh370JGq\n5cuX06tXL1566SXWrFnzyO1iMpmYNGkSFy9eZMiQIcTHx9OoUSMADAYDH3/8MV27dqV79+5MnDgR\nnU4H3DtyFB4eTv/+/Wnfvr1l+91v0aJFbN26lRUrVvDpp59iNBqZOXMmnTp1omvXrkycOBG9Xm+1\nfCHyW1paGnfv3iU1NdVS1qNHDz788ENMJtNjp//qq6/46quvHipPSUnhX//6F0FBQfTq1YtJkyZh\nNpsf2Ubdv2+PGTOGtm3bWh2hfffdd1m+fLlln9+zZw/du3e31CcnJ/P888+TlJSUbRuXnf79+6Mo\nCkOHDuXQoUOcP3+esLAwXnrpJbp3787PP//8UFskbEQRT+SXX35R+vTp88hxvv76a2Xq1KkPlc+a\nNUuZMmWKoiiK8sorryjt27dXevToYXnt2LHjkfPV6/VKq1atlO3btyuKoijR0dFKt27dFJPJpPj4\n+Cg3b960jJs5/Oeffyp16tRRYmJiFEVRlCFDhij9+vVTMjIylJs3byp169ZVEhISlD///FN56aWX\nLNPfP5y5PmazWXnllVeU8+fPK4qiKAkJCUqdOnUsy70/hvbt2ytHjx5V9u3bp3Tr1s0y3759+yoR\nERHK+fPnlW7duim3bt1SFEVRTp06pbRq1UpJSUmxWufjx48r/v7+yvXr1xVFUZQPP/xQad++vVVc\nRqNRqVu3rnLt2jVFURRl/fr1ysqVKxVFUZT33ntPWbRokWX8Tp06KQaD4aG/U/v27ZWPPvrIsl4t\nWrRQTp069cjtcv/7S5cuKQ0bNlQURVG++uor5Z133lH0er1iMpmUCRMmKB9++KFlObNmzbIsp379\n+srFixcf+lvfH/eSJUuUAQMGKGlpaYrJZFJGjhyprF+//qHYhMhv33//veLn56d06NBBGTt2rLJ6\n9WolNTXVUu/j46N069bNqp1LTEx85DzXr1+vvPbaa4qiKIrRaFQmTpyoXLhw4bFt1P379ldffWXZ\nt+/cuaM0a9ZMSU5OtmrLMtsoRVGUZcuWKWPGjHlkG/eo/e3+tq9nz57Kf//7X0VRFOXKlStKYGCg\ncvfuXat9WtiGnLp6Qmq1GrPZnKtpVSoVjo6OluGcnro6deoUarWadu3aAVCvXr0nOkJUuXJlfH19\nAfDy8sLNzQ17e3s8PDxwcXEhKSnpieNfsGABO3bsYPPmzZw9exZFUUhLS8t2mubNm5ORkUF0dDRO\nTk7cunULf39/li9fzvXr13n11Vet5n/x4kVq165tKdu3bx+tWrWiTJkyAPTr1489e/ZYLUOj0dC5\nc2dCQkJo164drVq1svq1dr+GDRui1Wb9cc+8xqZcuXIEBASwb98+atWq9UTb5n67du3i3Xffxc7O\nDrh3Pdbbb79tqQ8MDLQsx9PTk6SkJKpUqZLt/Pbu3UtQUJDls5P5C3P//v05jk2IvDR48GCCg4M5\nePAgBw8e5LvvvuO7775jzZo1liPeOT111aRJE+bMmUNYWBgtW7Zk0KBBeHt7k5CQ8Mjp7t+3+/Tp\nQ9++fZkwYQKbN2+mffv2lnjgXlvTt29f1q9fT/369Vm3bh3jxo3LVRt3vzt37hAbG0twcDAAFSpU\n4I8//njidRfPlpy6ekJ+fn6cO3fOcioi07Vr1xg2bFi2F47CvQtpc/PFmUmj0aBSqazKTp06hdFo\ntCrLvCg6k729vdVwVl/0KpUKRVEswwaD4aFxUlNT6dWrFzExMfj6+jJ+/Hi0Wq3VdFnNt2/fvmzc\nuJG1a9fSt29fVCoVZrMZf39/Nm7caHn9+OOPD53DfjCuzPP+D5o9ezYLFizAy8uL7777jtGjR2c5\nnrOzc7axqtX/7AaKoqDVap9ouzzowUTYbDZbTefg4GB5/+D8s/Lg3ysxMZHr168/Ng4hnqXIyEgW\nLVqEq6sr7du3Z/z48WzZsgWVSmV1vV5OValShd9//51hw4ah0+kYPHgw27Zte+y+eP++XalSJXx9\nfdmxYwfr1q2zJB7369OnD//73/84ceIEd+/epXnz5rlq4+6Xua/e306fO3fukd8LIv9IovOEypUr\nR/fu3fnggw8syY5Op2PKlCmULFnS6ojN/VavXk18fDxdunTJ9bKrV69u1YjExMQwaNAgzGYzHh4e\nlnPSmzdvzvG8PTw8uHLlCjdv3kRRFLZs2fLQOHFxceh0OkaNGkWHDh3Yv38/er3e8sWu0WgeSroA\nevXqxbZt2/j111/p3bs3AC1atCAiIoKzZ88CsHPnTnr06EFGRobVtC1btiQiIsLya279+vUPzf/W\nrVu0bduWkiVL8uqrrzJq1CjLtsgupqxkzvvKlSvs3bsXf3//R24XjUaTZeLTunVrVq5cicFgwGw2\ns2zZMlq1avVEMWTF39+fzZs3W7b1lClT2LJlS7bLFyI/eHh4MH/+fA4dOmQpu3HjBjqdDh8fn1zP\nd/ny5bz//vsEBAQwbtw4AgICOH78+BO1Ufd7+eWX+e6770hPT6dJkyYP1ZcrV44GDRowefJk+vbt\nCzy+jXscV1dX6taty4YNGwC4evUqoaGh3L17N0dtkXg25NRVDnz00UfMmzePkJAQNBoNer2eF154\nwepW359//pnIyEjL0Ytq1arxn//8x+rXfHa2bt3KypUrH7pzyd7envDwcGbMmMFnn32GnZ0d4eHh\n2NvbM2nSJKZNm4a7uzstW7a0nOp5Us899xwhISH06dOHMmXK0K5du4dut65Vqxbt2rWjS5cu2Nvb\n4+Pjw3PPPUdcXBxeXl507tyZsLAwwsPDraYrU6YMvr6+GI1GypUrB0DNmjWZNm0ao0ePthw9mT9/\n/kNHXGrVqsW4ceMYNGgQLi4u+Pn5PRS7h4cHw4cP59VXX8XR0RGNRsP06dMBaNOmDbNmzXqibZCR\nkUGvXr0wGAxMmjSJatWqAWS7XWrWrImDgwN9+/a1uq10+PDhfPrpp/Ts2ROj0Yifnx8ffvjhE8WQ\nlZCQEC5fvkzv3r1RFIVmzZoRFhZGSkqKZfmrV69+6GifEM9StWrV+Oabb5gzZw4JCQk4ODjg5ubG\njBkzqF69+mOnz7wQeeTIkVblPXv25MCBA3Tt2hUnJycqVqxIWFgYJUqUeGwbdb8OHTowderUbG/s\nAAgODmbkyJHMnz8feHQb9+CR8ex88cUXTJ06laVLl6JSqfjkk08oU6aMVVv0xhtvPNG8RN5SKU96\nbE4IIYQQopCRU1dCCCGEKLIk0RFCCCFEkSWJjhBCCCGKrAJ9MXJ6ejrHjh2jTJky2d5eLISwLZPJ\nxI0bN6hXr162dx8WRNK+CFGw5VXbUqATnWPHjjFgwABbhyGEeALLli2jadOmtg7jiUn7IkTh8LRt\ny1MlOkeOHGH27NksXbrUqnzbtm188803aLVa+vTpw8svv5yr+WfeKr1s2bJHdpomhLCdhIQEBgwY\nkONHGzyOtC9CFG951bbkOtH57rvv+Omnn3BycrIqNxgMzJw5kzVr1uDk5ERoaCgdOnSgdOnSOV5G\n5uHk8uXLU7ly5dyGKoTIB3l5+kfaFyFEpqdtW3Kd6Hh5eREeHs748eOtys+ePYuXlxclSpQA7vVf\ncvDgwad6MrAQRY3JrJCabkCXauBuqh5dqoHUDAMZehMZBhN6g8ny/v5/jSYzJrOC2axgMiuY/h7+\np+zvYdO9Mrj3mCxFufeyGs4MRgEFhcwnail/j5BZryiZ/7tX9+CTt9J1N/N8++Rn+zJ10T6c3B6d\nKKl4wocyPsFoarUKO40ajUaFVv33vxo1Ws0/7zXqv8u0ahztNTg5aC0vx8z39lqcHLU4O2op4eqA\ng51cZyREVnKd6HTq1In4+PiHynU6nVUnai4uLg/1D5WV8PBw5s6dm9twhCgwTCYzCbdSuXJDR2JS\nOjfvpJGYlMbNpHRuJqVxOzmDlHTDQwnD09KoVfdeGhVqtRq1SoVKBZkPTlah4u//7g2r7pVmvlf9\nU2g1fO+96qH5ZFKewRdsfrYviXfScTCkZjvtk/6ZnuzvqWAyg9FkxmzO2w+Ak4OWkq4OlHRzoISr\nPR7ujpT3dKG8pzPlPV0o5+GMs6Ndni5TiMIgzy9GdnV1JSUlxTKckpJi1TBlZ8SIEVZdKQDEx8db\nenwWoqBRFIXrt9M4FXebi9fucun6XS5du8uVGykYTVn3kePmbIdHCUe8K7jj5myHq5M9rs52uDnb\n4+yoxcFOg4O95r5/tTjYa7C3U2Nvp8FOq0ajVmeR1GCzriDi4+MJXJs/y3oW7Uv42PY2OXV1/1E5\no1nBaDRjMpsxmu6VGUxmjEYz6XoTaRlG0vVG0tKNpOmNpGXce5+uN5GSZuCOLoOkv18nL6Zmm0S5\nOdtTuawrNSqVoEblEtSoXJIq5dzQauRJI6LoyvNEp0aNGsTFxXHnzh2cnZ05dOgQQ4YMyevFCJHv\nTCYz568kc/zCTU6cv8Xx87e4lWzdO7GTg4bqldypXNaNymVdKVPSCc8STniWdMSzhJOcXnhKRal9\nUatV905jafM2yTCbFXRpBm4mpZFwM4WEm6n3/r2VSkJiCifjbnHiwi3L+FqNmqoV3Kjt7UG950pT\nr7onJVwf3zefEIVFniU6mzZtIjU1lX79+jFhwgSGDBmCoij06dPH0qGjEIXNreR0Dp24xqET14g6\ndZ20DJOlrqSbA/71K1CnqgfeFdzxKueGZwlH6WTzGZD25cmp1SrcXexxd7GnWsUSD9Wn643EXU3m\n7OUkzl1O4mz8HS5cvcuZ+CQ2R5wHoHqlEjzvW47n65SjZpVSqNXymRaFV4Hu1DPz0PLWrVvlrgiR\nb5J0GUQcvcKuvy5z/PxNy7UXFUu7UP+50vhW86BOVU/KezpLUkPh3U8La9zPgsFo5vSl20SfTeTo\n6USOn7+J0XTvg1+6hCMBDSvRumElalYpKZ95kW/yah8t0A8MFCK/mMwKf528zm/74zgQk4DJrKBS\ngW81T1rUq8DzvuWoVMbV1mEK8UzYadX4VvPEt5on/V6oRWq6gahTNzhwPIE/jyWwYedZNuw8S+Wy\nrrzY3JsOTavI6S1RaEiiI4q11HQDv+2PY9Puc1y/nQZAtYrudGjqReuGFfEs4fSYOQhR9Dg72tHS\nryIt/SpiMJr569R1dkbGszf6Kt9viuE/Px+ndcNK9Gr3XJanx4QoSCTREcXSnbsZrN9xhv/tu0Ba\nhhEHew2dWnjTqYU3z1WWw/NCZLLTqmnmW55mvuVJTtGzI/IS/9t3ge2R8WyPjKdBzdL0e6EW9Z/L\n+UMbhcgPkuiIYiVJl8G67WfYsvc8GXoTpdwc6NuhJl1aVsXN2d7W4QlRoLm72NOjTQ26BVTn8Mnr\nrN9xhiOnEzlyOpH6NUrTv1Mt6tWQhEcULJLoiGJBbzDx0+5z/PjHKdIyjHi4OzL4JV9ebOGNnVZu\n+RYiJ9RqFU3rlKNpnXKcunibZb/Gcjj2Ou/PS6SZb3kGd/elctnHP99IiPwgiY4o0hRFsVxXcP1W\nKm7OdrweVI8u/lWxl2faCPHUfLxKMXWoP7EXbvHDluMcOJ5AZOw1urSsyoDOdXB1kqcxC9uSREcU\nWddupbJg3VEOnbiGVqOiZ9sa9HvBB1c5RSVEnqtd1YOZb7Xiz2NXWbzpOJv3nCfiyBVeD6pH64aV\n5Lo3YTOS6Igix2RW+GnXWf77Syx6g4kGNUszvE8DuT1ciGdMpVLhX78iTeuUZ/2OM6z6/SSf/zeS\nrQcv8U5wQ8qUkrsYRf6TREcUKVcTU/hq1V/EnLtJCVd7RgQ3oG3jyvJrUoh8ZKdV8/ILPrRuWIn5\na49w+OR13pm9jWE969OhaRXZH0W+kkRHFAmKovDb/oss2hhNut5EK7+KDO/jJw81E8KGKpR2Yeow\nf/44cJHvNh7jy5V/sS/6Kv/q1wh3FzmFLPKHJDqi0EtJM/DNmiPsjrqMi5MdYwc0pE0juSZAiIJA\npVLRsbk3DWqW4atVf7E/JoF/fbGdsQOayK3oIl/kbbe5QuSzM5fuMGrODnZHXaZOVQ++HtNOTlUJ\nUQCV9XDm4zdaEtalDrfvZjBxfgQrfz+J2Vxgu1sURYQc0RGF1h8H4pi39ihGk5ngwJoM6FQbjUZy\ndyEKKrVaxcsv+FCvhief/zeSZb/EcuribUb3byK3oYtnRr4VRKFjMJqZt+YIX62Kwt5Ow+QhLRjY\n1VeSHCEKCd9qnnw1uh0Nfcpw8Pg1Rn+5k7irybYOSxRR8s0gCpW7qXo+WriP/+27QNUK7swZ1Zam\ndcrZOiwhRA65u9gzZag/fTvU5GpiCuPCdxEZe83WYYkiSBIdUWhcTUxh3Ne7iT6biH/9Cnw+ojUV\nSrvYOiwhRC5p1CoGveTL+LCmmEwK0/5vP//be97WYYkiRq7REYXCifO3mL54P8kpenq3e45BL/mi\nVssFx0IUBa0bVqJMSSemL97PvLVHuZKYwuBudWUfF3lCjuiIAm931GUmLohAl2bgrb4NGNxdGkAh\nipraVT2Y/a82VC7ryoadZ5n1n4Ok6422DksUAZLoiAJLURRWbz3FZ0sPodWomTykOV38q9o6LCHE\nM1Le04XPR7Smfo3S7Iu+ygfzIrh9N93WYYlCThIdUSApisJ3G4/xn59PULqEI5++E0CT2nLRsRBF\nnauzPVOH+dOhaRVOX7rD+PDdJNxMsXVYohCTREcUOCazwjdrjrBp9zmqlHNj9sg2VKtYwtZhCSHy\niZ1WzaiQRvTr6EPCzVTem7ubuAS5/VzkjiQ6okAxmcx8ueIwv/4ZR/VKJZj5Vis8S0iPx0IUNyqV\nilc61+H1oHrcSs7g/W/2cOribVuHJQohSXREgWEwmvl06SF2HI6nlncpPhneSjrlFKKYC2pTg5H9\nGpGSZmDSggiOnL5h65BEISOJjigQMgwmPlm8n33RV6lfozTThvnLI+GFEAC80MyLCYOex2BUmPLd\nn+yLvmrrkEQhIomOsLm0DCPTFv1JZOx1Gtcuy0dDW+DsKEmOEOIf/vUrMuX1Fmg1Kmb95yDbDl2y\ndUiikJBER9iULs3ARwv3cfTMvacdTxrcDAc7ja3DEkIUQA18yjD9zZY4O2iZs+Iw/9t3wdYhiUJA\nEh1hM0m6DCYtiODEhVu0bVSZ98KaYqeVJEcIkb1a3h7MeKsVJV0dmLfmCBt2nrV1SKKAk0RH2MTt\n5HQ+mB/B2fgkXmzuzbv9G0vv40KIJ1KtYglmvNUKD3dH/u+nY6z6/SSKotg6LFFA5bqvK7PZzJQp\nUzh58iT29vZMnz4db29vS/306dM5fPgwLi73Ol2cN28ebm5uTx+xKPRu3E5j0oIIriSm0L11dV7v\nUU+6dBAW0raIJ1GlnBufvhPAxAV7+e8vsaTrTQzsWgeVStoSYS3Xic4ff/yBXq9n1apVREVFMWvW\nLObPn2+pj4mJYdGiRXh4eORJoKJouJqYwqQFEVy/nUZwYE3CukjDJKxJ2yKeVHlPFz59O4BJCyJY\ns+00GQaT/HASD8n1uYLIyEhat24NQMOGDTl27Jilzmw2ExcXx+TJkwkJCWHNmjVPH6ko9C5du8uE\nb/Zw/XYar3SpzcCuvpLkiIdI2yJyonRJJ2a+HYB3eTc27T7H3NVRmMxyGkv8I9dHdHQ6Ha6urpZh\njUaD0WhEq9WSmprKK6+8wuDBgzGZTAwcOJB69epRu3btbOcXHh7O3LlzcxuOKODOX0niw2/3kqTT\nM6RHPXq2rWHrkEQBlddtC0j7UtSVcnNkxlsBfLRwL78fuIjeYGZUaCO0ct2f4CmO6Li6upKS8k9H\na2azGa32Xt7k5OTEwIEDcXJywtXVlRYtWhAbG/vI+Y0YMYKTJ09avbZu3Zrb8EQBcuribd6fF0Fy\nip63+jaQJEc8Ul63LSDtS3Hg7mLP9DdbUaeqBzv/iuezpYcwGE22DksUALlOdBo3bsyuXbsAiIqK\nwsfHx1J34cIFQkNDMZlMGAwGDh8+TN26dZ8+WlHoxJy7yaQFe0lLNzAqpDFd/KvaOiRRwEnbInLL\nxYwC4IwAACAASURBVMmOacP88XuuNPuirzJ98QHS9UZbhyVsLNenrjp27EhERAQhISEoisKMGTNY\nvHgxXl5eBAYGEhQUxMsvv4ydnR1BQUHUrFkzL+MWhcBfJ68zffEBTCYz48Oep1WDirYOSRQC0raI\np+HooGXy6y2YteQgh05cY9qi/Ux6rZk8bb0YUykF+OED8fHxBAYGsnXrVipXrmzrcEQOHIhJYOaS\ng6hU8P6g53net7ytQxLPSGHdTwtr3OLJGIxmZi87xN6jV6nlXYopQ6X/vMImr/ZRuVJL5LkdkZf4\n5IcDaDQqPhrSQpIcIUS+s9OqGf9KU9o3qczJuNtMnB9Bki7D1mEJG5BER+SpLXvO8cXywzg5aPl4\nWEsa+JSxdUhCiGJKo1EzKqQxnVp4c+5yEu/Pi+BWcrqtwxL5TBIdkScURWHV7ydZsD6akm4OzHyr\nFXWqyQPdhBC2pVareLtvA3q0qX7fs7xSbR2WyEeS6IinpigK32+K4b+/xFK2lBOfvh1AtYolbB2W\nEEIAoFKpeL1HPfq94MPVxBQmfLOHK4k6W4cl8okkOuKpmExmvl4VxYadZ6lSzpXPRrSmYhnXx08o\nhBD5SKVS8UqXOgzsWocbt9N4/5s9XExItnVYIh9IoiNyLT3DyMwlB/nj4EWeq1KSmW8F4FnCydZh\nCSFEtoIDfRgaVI9byRlM+CaC2Au3bB2SeMYk0RG5cjs5nffnR7A/5v/bu/P4pup8b+CfLE3TNmna\ndEv3lVJoKaWggAgIBUWEAZVNEETFRx1FBR+YuY46OFe5ouNznQHBy+igMs6oXEd0HEcRBkVBEYGC\nhbbQlu5bujddkiY5zx9pQ0ORpaQ9Sfp5v155tTknTT6nkG+/Oef8zq8a6UnBeOGhG6BReYsdi4jo\nsn4xJRGrF2WgrbMLT207iG+yK8SORAOo3xcMpKGrpKoFz735PfSNHci6LhqPLMiAl5w9MxG5j5vH\nxyJIo8Smd37ESzt/RHV9GxZMH8aJhj0Q/zrRVTmeX4v1W76BvnsG8scXj2GTQ0RuaWxKGDY9eiOC\nNUq881kutuw6AbPFKnYscjL+haIr9sX3JXjuje9h6rLi/y4bi8UzhvPTDxG5tfgIDX7/+BQkRGqw\n57CtxrV1dIkdi5yIjQ5dlsVixZuf5GDLrmz4Kr3w/EM3YGomL5lPRJ4hSOODFx+5EdeP1CH7jB7r\nNn+DSj2Hn3sKNjp0Sc0GI57d/h12f12IyBAVfv/YZKQmBIkdi4jIqXy85Xjq3uvxi8m2CwuuefVr\n/HCqWuxY5ARsdOhn5Zc04In//honC+owPlWH//fEFF4jh4g8lkwqwQPzR2HNXZkwm634zz8fxl8+\nz4XF6rJzX9MV4Kgr6sNqFbD76wK881kurIKAu29NwcLpyZBKeT4OEXm+6eOiEatTY+PbR/D+l2dw\nqqge/3fZWF4nzE1xjw45aGztxHNvfo8dn56Gv58C//ngDVg8YzibHCIaUhKjAvCHNVMxcVQ4cgrr\nsfr3X+GH0zyU5Y7Y6JDdwROVeOSl/TiWV4vMlFD88clpGD2Ms48T0dCk8lXgP+65Dg/dkY5Okxn/\n+eZh/PH942jv5Kgsd8JDV4RmgxHbd/+EA8croPCS4YH5aZgzKYF7cYhoyJNIJLhtUjxSE4Lw3389\nhi9/KEX2WT0eW5SBjORQsePRFWCjM4QJgoC9P5Rix6en0NreheGxgVhzVyYiecIxEZGDuHB//P7x\nKXh/bz527TuLZ/7nO9yUGYX7f5GGADWnv3FlbHSGqKKKZmzf/RNOFdXDx1uGVfPSMGdSPGQyHs0k\nIroYL7kUd88agQlp4Xjtf0/gq2PlOJJbg+W3jsCsCbGsny6Kjc4Q09DSib/8Kxd7j5RCEIAJaTo8\neHs6ggM4moCI6EokRQXg949NwWcHz2Hnv3Lx+t9P4p8Hi3Df3DSMGxEmdjy6ABudIaLZYMRHXxXg\n04PnYDRZEKtT475fpCFzOI8xExFdLZlUgrmTE3BjRgTe/TwPX3ZPHzEqMRhLbxmOtMRgsSNSNzY6\nHq6+uQP/+KYI/zx4Dp0mC7T+Sqz6RRpmjo+FjCcbExFdk0C1Eo8uzMCcGxPw1qencDSvFv+xtQ7p\nScFYlJWM9GHBnBNQZGx0PFRRRTM+PlCIA8fLYbYI0Pp7Y8XskbhlQiwUXjKx4xEReZS4cH9seGAi\n8koa8NfP83D8jB4nC+oQH+GP+VMTcePoSNZekbDR8SDtnV04cLwCew6X4GxZEwAgKlSFeVMSMW1c\nNLz5JiMiGlApsVr87sEbcKa0ER99VYBDJyvx3387jjc+zsG0cdG4eXwsYnX+YsccUtjouLlOkxlH\nc2txILscP56ugclshVQCXDcyDLdOjMPYlDBeD4eIaJAlxwTiVyuuQ01DO/516Bz2HSnDJweK8MmB\nIsRH+GNyRiQmZ0RCF+QndlSPx0bHzQiCgJqGdhzLr8WR0zU4eVYPk9kKwLb3ZmpmFGZcF8NRVERE\nLiBM64uVc1KxbNYI/HC6GvuOlOJ4fi3e+SwX73yWixidGteNCMPYEWEYHhPIw1sDgI2OizN2WVBc\n2YzCimbkFTfgp8J61DV12NfH6tS4PlWHyRmRiAv350lvREQuyEsuxaT0CExKj4Ch3YTvc6pw8GQV\nTp7V48P9BfhwfwHkMgkSowIwMj4II+K0GBmvhUbFixFeKzY6LsLUZUFNQztqGtpRXtuKwopmFFU0\no7ymFVbh/OPUvgrckB6O9KQQXDciDKFaX/FCExHRVVP5KjDj+ljMuD4WnSYzfiqoQ/YZPU4XN+Bs\nWRPySxrxUfdjw7S+iA5TIyZMjegwFaLC1IgOVcPPx0vUbXAn/W50rFYrNmzYgPz8fCgUCjz//POI\njY21r//ggw/w3nvvQS6X4+GHH8a0adOcEtidWK0CjF0WtHd2oaXNhKZWI5oNRjQZTGhq7URjqxE1\nDe2orm9DfXNnn59XKmRIidMiIVKDxMgADIsOQHSYmufckEdjbaGhRKmQ47qROlw3UgcA6DSacaas\nEbnnGnC6uAFF5c34MbcGP+bWOPycRqVAkMYHQRolgjU+CApQIsjfBwFqb6h9vaDyVUDl4wWVj9eQ\nv2JzvxudvXv3wmQy4f3330d2djZefPFFbNu2DQCg1+uxc+dOfPjhhzAajVi6dCkmTZoEhULRr9c6\ncLwc2jKz/b7Qe6UgXHS58DN3fu4xQq81VitgsVhhtlphsQgwW6wwWwTbMosVFquALnP3OqsVRpMF\nHUYzOk1mdHR2fzWa0WmyOOa4CKkECA7wQXpSMMK0vggL8kVEkAoJURqEB/mxqaEhZzBrC5GrUXrL\nkZ4UgvSkEPuyljYTympaUV7birIaA8pqWlHT0IYKvQFFFc2XfU4/pRxKbzm8vWRQeMngrZDB+4Kv\nCi8ZZFIJZFKp7atMAmnv+xcsk0oASCTo/tJNYv/+/HLbAonkIvftP+b4PBLYVtbVOjZ3/dXvRufo\n0aOYPHkyACAjIwM5OTn2dSdPnsSYMWOgUCigUCgQExODvLw8pKen9+u13vksF16+ztnggaRUyODj\nbfsPFaBWwsdbbr/5+ykQoPaGRuWNgJ6b2hvBAT7wkg/tbpuot8GsLUTuwN9PgdSEIKQmBDksFwQB\nbZ1m1Dd1oL65E3XNHWhpM8HQbkJrexda200wdH81mizoNJnRbDDC2GWB2XKZT+EuoKu9wSnP0+9G\nx2AwQKU6P8u1TCaD2WyGXC6HwWCAWq22r/Pz84PBYLjk823evBlbtmy56LpV89IQHKLr3Tai936O\n3uffSuBw5+oeY3+sBHKZBDKZFHKZBHKZFHKZrauVy6SQ9V4mk8DbSwalQs69L0RO4OzaAly6vhC5\nK4lEYj88FRt+ddfmsVisMHZZbDeT7avVKsBiEWCxWm1HNqy2IxgWq9C9zvG+ANgPjQjofZREgCBc\n5H7PPaHnJ3B++UWep6GuGs//+6p/LX30u9FRqVRoa2uz37darZDL5Rdd19bW5lCcLmb16tVYvXq1\nw7Ly8nJkZWVhQlo4oqKi+huViNyIs2sLcOn6QjQUyWRS+Mqk8FW67knN5eVeeN4Jz9PvRiczMxP7\n9+/H7NmzkZ2djeTkZPu69PR0vPrqqzAajTCZTCgsLHRYf6UsFgsAoLq6ur8xiWiA9bw/e96v12ow\nakvvvKwvRK7JWbWl343OzJkzcfDgQSxZsgSCIGDjxo3YsWMHYmJikJWVheXLl2Pp0qUQBAFr1qyB\nt/fVXwtAr9cDAJYtW9bfmEQ0SPR6vcPoqP4ajNrSkxdgfSFydddaWySCcLlxQeLp7OzE6NGjsWfP\nHshknn21yKysLOzbt0/sGINiqGzrUNlOi8WCm2++GSdOnIBSqRQ7zhVjffFMQ2Vbh8J2Oqu2uPQF\nA3s2zBmfEt3BUDoPaahs61DZTgBu1eQArC+ebKhs61DZzmutLRzXTERERB6LjQ4RERF5LDY6RERE\n5LFkGzZs2CB2iMsZP3682BEGxVDZTmDobOtQ2U7AfbfVXXNfraGyncDQ2VZu55Vx6VFXRERERNeC\nh66IiIjIY7HRISIiIo/FRoeIiIg8FhsdIiIi8lhsdIiIiMhjsdEhIiIij+U2jU5hYSHGjh0Lo9Eo\ndpQB0draioceegh33303Fi9ejOPHj4sdyamsViueffZZLF68GMuXL0dJSYnYkQZMV1cX1q1bh6VL\nl2LBggUeP/FefX09pk6disLCQrGj9Iun1xaA9cVTDLXaAjinvrj0pJ49DAYDNm3aBIVCIXaUAbNj\nxw5MmDABK1euRFFREZ588kl89NFHYsdymr1798JkMuH9999HdnY2XnzxRWzbtk3sWAPik08+QUBA\nAF5++WU0NTVh/vz5yMrKEjvWgOjq6sKzzz7rdhN69hgKtQVgffEUQ6m2AM6rLy6/R0cQBDzzzDNY\nu3YtfHx8xI4zYFauXIklS5YAsE1N7+3tLXIi5zp69CgmT54MAMjIyEBOTo7IiQbOrFmz8PjjjwOw\n/f+VyWQiJxo4mzZtwpIlSxAaGip2lKs2VGoLwPriKYZSbQGcV19cao/Orl278Pbbbzssi4iIwOzZ\ns5GSkiJSKue72HZu3LgR6enp0Ov1WLduHZ566imR0g0Mg8EAlUplvy+TyWA2myGXu9R/Qafw8/MD\nYNvmxx57DE888YTIiQbG3//+d2i1WkyePBnbt28XO84lDZXaArC+AJ5bX4ZKbQGcW19cfgqImTNn\nQqfTAQCys7ORnp6Od999V+RUAyM/Px9r167F+vXrMXXqVLHjONV//dd/YfTo0Zg9ezYAYMqUKThw\n4IDIqQZOVVUVHnnkEfuxdE+0bNkySCQSSCQS5ObmIi4uDtu2bUNISIjY0a7IUKotAOuLpxgKtQVw\ncn0R3Mi0adOEzs5OsWMMiLNnzwq33HKLkJubK3aUAfH5558Lv/rVrwRBEITjx48L999/v8iJBo5e\nrxdmzZolHDp0SOwog+buu+8WCgoKxI7Rb55cWwSB9cVTDMXaIgjXXl88a7+eG3vllVdgMpnwwgsv\nAABUKpVHnUw3c+ZMHDx4EEuWLIEgCNi4caPYkQbM66+/jpaWFmzduhVbt24FAPzpT39y2xN2yf2x\nvngG1pb+cflDV0RERET95fKjroiIiIj6i40OEREReSw2OkREROSx2OgQERGRx2KjQ0RERB6LjQ4R\nERF5LDY6RERE5LHY6BAREZHHYqNDREREHouNDhEREXksNjpERETksdjoEBERkcdio0NEREQei40O\nEREReSy52AHIM5SXl2PmzJlITk62LxMEAStWrEB0dDQeeOABxMfHQyKRQBAEyGQyPProo5g+fToO\nHz5sX9/bkiVLcNdddw32phCRi5o+fTr+8Ic/ICwsDBs3bkRhYSEAQKlU4sEHH8SMGTMAAMuXL0dF\nRQXUarXDz3/88ceDnpnEx0aHnEapVDoUkpqaGsyZMwerV69GTEyMw7q8vDzcdddd2LdvHwD0WU9E\n9HOefvpp3HDDDXj11VcBAAUFBbjrrrsQHx+PxMREAMD69esxa9YsMWOSi+ChKxowYWFhiI2NRWho\naJ91KSkpUCqVqKioECEZEbkzvV6Pzs5OWK1WAEBSUhK2bdsGf39/kZORK+IeHRowx48fR2lpKTo7\nO/us27NnD6RSKZKSknDy5EmUlpZi3rx59vXh4eF4/fXXBzMuEbmJ9evXY926ddixYwcyMzMxduxY\nzJ07FyEhIfbHvPTSS9i2bZv9/tq1azF16lQx4pLI2OiQ03R2dtqbFYvFgsDAQLz88stQKpUOjYzZ\nbIZOp8PWrVvh4+MDgIeuiOjKTZw4EV999RWys7Px448/Yv/+/Xjttdfw9ttvIz09HQAPXdF5bHTI\naS48R6fH4cOH2cgQkVMIgoANGzbgmWeewbhx4zBu3Dg89NBD+M1vfoPdu3fbGx2iHjxHh4iI3IZE\nIsGhQ4fwzjvvQBAEAEBHRwdKS0sxcuRIkdORK+IeHSIicitvvvkmXn75ZezcuRO+vr6QSCS4/fbb\nsWDBArGjkQuSCD0tMREREZGH4aErIiIi8lhsdIiIiMhjsdEhIiIij+XSJyN3dnYiJycHISEhkMlk\nYschoouwWCzQ6/VIS0uDUqkUO84VY30hcm3Oqi0u3ejk5ORg2bJlYscgoivw7rvvYty4cWLHuGKs\nL0Tu4Vpri0s3Oj2X83733Xeh0+lETkNEF1NdXY1ly5Y5XH7fHbC+ELk2Z9UWl250enYn63Q6REVF\niZyGaOgwdVnQ1tmFQPWV7y52t8M/V1pfLBYrmgxGBGl8BisaEfVyrbWFJyMTUR9bdmXj4U3/hsXK\ny2y9+0UeVr2wF80Go9hRiKgf2OgQUR9ltQaYuiyQSsROIj5TlxVmixU1De1iRyGifmCjQ0R9NBuM\nCFB7QyJhpxOg9gYA7tEhclNsdIjIQZfZiobmTgTznBQAgNbfdp6SvqlD5CRE1B/9Ohm5q6sLTz31\nFCoqKmAymfDwww8jKyvLvv6tt97Crl27oNVqAQDPPfccEhISnJOYiAZUVZ0BFquAqFCVKK/vavUl\nIsQPAFChNwzYaxDRwOlXo/PJJ58gICAAL7/8MpqamjB//nyHQpSTk4NNmzYhLS3NaUGJaHCUVLcC\nAKLD1KK8vqvVlzidP6QSoKCsaVBej4icq1+NzqxZs3DLLbcAAARB6DP069SpU9i+fTv0ej1uuukm\nPPjgg9eelIgGxU+FdQCAEXFaUV7f1eqL0luO2HB/FJQ1odNkhlLh0lflIKIL9Osd6+dn25VrMBjw\n2GOP4YknnnBYf9ttt2Hp0qVQqVR49NFHsX//fkybNu2Sz7l582Zs2bKlP3GIyEksVgFHTlXDTynH\nsOgAUTK4Yn0ZNyIM5ypbcCyvFjekR/T7eYho8PX7ZOSqqiqsWLEC8+bNw9y5c+3LBUHAPffcA61W\nC4VCgalTp+L06dOXfb7Vq1cjPz/f4bZv377+xiOifsg+U4u65k7cmBEJmUy8sQquVl8mdTc3e4+U\nXv3GEJGo+lXJ6urqcN9992HdunVYsGCBwzqDwYA5c+agra0NgiDg8OHDPFeHyA0IgoD39uQDAGZN\niBMthyvWl4RIDUbEaXHkdA2KKpoH/PWIyHn6dejq9ddfR0tLC7Zu3YqtW7cCABYuXIiOjg4sXrwY\na9aswYoVK6BQKDBx4kRMnTrVqaGJyPm+OlaOvJJGTBwVjiSRDlsBrllfJBIJltw8HL/d/h22fXgC\nLz46GTJeTZHILfSr0Xn66afx9NNP/+z6+fPnY/78+f0ORUSDq1JvwOt/PwkfbxnunZMqahZXrS+Z\nw0MxOSMS32RX4L09+Vg2K2XQMxDR1eMFA4mGuPrmDjyz/Tu0d5rx0B3pCA/2EzuSy3rw9lEIDfTB\ne1/mY8/hErHjENEVYKNDNISVVrdg/ZZvUdvQjqW3pGD6uBixI7k0jcobGx6YCLWvFzZ/kI2/7y+A\nIHDiUyJXxkaHaIj66lg51m3+xtbk3DwcS2Ymix3JLUSHqbHxlzdC66/Ejk9P4eW/HIWho0vsWET0\nM3jlK6Ihpq6pA29+koNvT1RCqZDhyaWZuGlstNix3EpcuD9+/9gUbNp5BN9kVyCvpAEPzBuFCWk6\nToRK5GLY6BANEe2dXdj9dSE+3F8AU5cFKbGBWLt0LM/J6aeQQB9seuRGvPflGezadwYb3/oBGckh\nuGf2SFFHrRGRIzY6RB6uqdWIT74pxGcHz6Gt04xAtTceviMd08dFQ8oh0tdEJpNi2awUTBkTiT/t\n/gnHz+iRfeZrjE0JxaIZyRgRp+UeHiKRsdEh8kCCIOD0uQbsOVyCb7MrYDJbEaDyxvJbh2Hu5AT4\nePOt70zRYWo8938m4sRZPd778gyO5tXiaF4tEiI0uPWGOEzNjOLvnEgkfOcReZCqujZ8e6IC+46U\nokLfBgAID/bDvCmJmHF9DLy9ZJd5BuoviUSCjORQZCSHIqewDp98U4TDp6rx2v+ewJ//kYMJaeGY\nMiYKGckhkIs4vQbRUMNGh8jNVegNOHiiEgdPVKKo0jY9gZdcipsyo3DzhFikJQTx8MkgS0sMRlpi\nMOqbO7DncCn2/lCC/UfLsf9oOdS+CkwaHYHxqTqkJwVDweaTaECx0SFyM51GM04W1uF4Xi2O5dei\nss6250Yuk2DciDBMSg/HhLRwqHwVIielII0P7uoeup9X3IgD2eX49kQlPv+uGJ9/VwxvhQyjk0Jw\n3cgwjBsRhuAAH7EjE3kcNjpELq7LbEVBWRNyiuqQfUaP0+caYLZYAQA+3nJMSNNh4qgIXJ+qg8rH\nS+S0dDESiQQj4rUYEa/Fql+k4fS5BhzJrcGR09X4ofsGAJEhfhiVFIL0xGCkJQUhUK0UOTmR+2Oj\nQ+RiOoxm5BU34FRRPU6dq8eZkkaYzFb7+oRIDcamhCJzeChS4rQ838PNyGRSjEoKxqikYNw3NxXV\n9W04croGx/Jrcaqo3r63B7Cd5DwqMQgj4rQYHquFLsiXhyGJrhIbHSIRWSxWlNa04kxpE86WNeJs\nWROKq1pgtdqmFZBIgFidP1ITgpAaH8RP+R5IF+SHuZMTMHdyAiwWKwrKm3CyoA4/FdThdHEDPjvU\nis8OFQMANCoFhsdoMTw2EClxgRgWHcjRXESXwXcI0SCxWgVU1bfhbKmtoTlb1oTCimaYuiz2x3jJ\npUiODrA1Ngm2T/I812bokMmkGB5r23uzMCsZXWYrCiuakF/SiPySRuSVNDgc6pJKgMhQFRIjA5AQ\nqUFilAYJERr+nyHqhY0O0QBo6+hCcVULiiubca6qBcWVLSiuboHRdL6pkUoliNWpMSw6EMOiA5AU\nHYBYnT+85DwURTZecilSYrVIidXal9U3d+BMaSPyihuRX9qIoopmlNWU46tj5fbHhGl97Y1PYmQA\n4sL9EaRR8rAXDUlsdIiugdliRVVdG0qqbc3MucoWFFc1o7axw+FxcpkEUaFqxEf4IykqAMOiAxEf\n6Q+lgm9BujpBGh9MHOWDiaMiANj2FFbXt6GwohmF5U0oqmhGYUUzvvupCt/9VGX/OT+lHDE6f8SG\n+yNWp0aszh8xOjU0Km+xNoVoULDKEl2BTpMZFbUGlNUaUF7TitKaVpTXtqKqrg1mi+Dw2EC1NzKH\nhyIu3B9xEf6Ij9AgMkTFPTU0IKRSCSJCVIgIUWFyRiQA25Wx65s77Y1PSXUrSqpbkF/aiNziBoef\nD1B792p8/BEdpkJkiIoNEHkMNjpEvRjaTSirMaCsthVlNa0orzWgrKYVtY3tEBz7Gfgp5UiMCkB0\nqBoxOtvemrhwDQLU/ANB4pJIJAgO8EFwgA/Gp4Xbl5u6LKjQG1BS1WJvfkqqW3HibB1OnK1zeA61\nrwJRoapeNzUiQ1XQaX0h40g/ciNsdGjIaW03oaquzXarbzv/fV0bmgzGPo8PUHsjLSEY0WEqRIep\nER2qRlSYClp/nvNA7kXhJUN8hAbxERqH5R1GM8pqWlFS1YLyWkP3rfWie4DkMgnCg/0QGdLd/ISo\nEBWmQkSwCv5+PAmaXA8bHfI4giCg2dDdzNQbUFnn2MwYOrr6/IxUKkFYoC8SojSICVMjKlSNmDA1\nosNUHMFCHs/HW47kmEAkxwQ6LO8yW1Fd34by2lZ7A1Shtx2+LasxAKh2eLzKxwvhwX6ICFbZvob4\n2e+rfb34wYBEwUaH3JLVKqCxtbNPE9PT3HQYLX1+Ri6TQhfkixHxWlvxDfJDeHdBDgn04YX3iC7g\nJZfa9mKGqR2WC4KAJoMRFfa9PwZU1bWhss6Ac5UtOFvW1Oe5/Hy8EBFsa3x6mp+e+/5+CjZBNGD6\n3ehYrVZs2LAB+fn5UCgUeP755xEbG2tf/8EHH+C9996DXC7Hww8/jGnTpjklMA0dFquAuqYOVNUZ\nuhuYdofve19/pofCS3a+mAadL6rhQX4ICvCBTMpi6upYW1yfRCJBoFqJQLUSaYnBDusufN/2fBi5\nZBOklCM8RGX78BHiZ3sPB9k+hGhUbILo2vS70dm7dy9MJhPef/99ZGdn48UXX8S2bdsAAHq9Hjt3\n7sSHH34Io9GIpUuXYtKkSVAoeAiAHJktVtQ2tDucK9NTGGsa+o5oAmy72aPDVNAF9RTE8w0Nz5tx\nf6wt7k0mlSBM64swrS8ykh3XWawC6ps6bO/z+jZU6s83QyVVLSi4SBPk4y3vtRfI9n7XdX8fqFZC\nyg8vdBn9bnSOHj2KyZMnAwAyMjKQk5NjX3fy5EmMGTMGCoUCCoUCMTExyMvLQ3p6+rUnJrdj6rKg\npqG9VxNjsJ8IXNvYYZ/uoDe1rxcSIjX2T3X2Isfd3B6PtcVzyaQShGp9Ear1xWiEOKyzWgXUNXfY\n60R1r8EC5bUGFFU093k+hZcM4UG+3TVC1euQNPfg0nn9bnQMBgNUKpX9vkwmg9lshlwuh8FgUzMq\nJwAAEvVJREFUgFp9/piun58fDAbDJZ9v8+bN2LJlS3/jkMg6jWbHEUz157/WNXX0GZoN2EYzDY8J\ndGhidN1FSs0TgIcsZ9cWgPXFHUilEoQG+iI00Bejhzk2QYIgoKGl86L1pVLfhpLq1j7P13NOnn3P\nb69baKAvz8kbQvrd6KhUKrS1tdnvW61WyOXyi65ra2tzKE4Xs3r1aqxevdphWXl5ObKysvobkZys\nvbOr78m/9bY9NA0tfYdlA0CwRom0hGDouj919YzG0AX5wlfpNchbQO7A2bUFYH1xdxKJBEEaHwRp\nfPqcEyQIAlraTA6HvXsGJfTsDbpQzyjL3s1PzyHwMK0vFF6ywdo0GgT9bnQyMzOxf/9+zJ49G9nZ\n2UhOPn8wNj09Ha+++iqMRiNMJhMKCwsd1pPrsjcz+jZU1htQqb/0NWakEiA40BcZw0Ici0b33hlv\nFgy6SqwtdDUkEgk0Km9oVN5IidP2WW9oN130HMCq+jYcy68F8i98PiA4wMfe+JzfG2S7WKKSs8W7\nnX7/i82cORMHDx7EkiVLIAgCNm7ciB07diAmJgZZWVlYvnw5li5dCkEQsGbNGnh782qxrqKto6vX\nm97xOjM/18yEan2RGRlqf+P3nAwYpvWFl5zNDDkPaws5k8pXgWG+CgyLDuyzrr2zC9X17fZa2NMA\nVde14WRBHU4W1PX5Ga2/su+ozu56yL3UrkkiCBc7e8I19Oxa3rdvH6KiosSO41YudqGvSr0BVfVt\naDaY+jzevis3xK/XEE/bdS5CAn05TxP9LHd9n7prbhocxi4Lqi+4cnrPaLG6xnZcZAwFNCpFrwao\neyBFkC/CecHEfnHWe5T74Nxcs8F4/mqltYbuC3i1orqhvc9oJmn3sM+kqIA+Vy/lyXlEROd5e8kQ\nq/NHrM6/z7ou8/mRpBdOJXO2rAl5JY19fsbPxwtR3dNlRIeqEa2zTScTqvXl6LABxkbHDfTMRFxS\n3YKSqtZee2la0dredzoDta8Cw2MC+0zGF6ZlM0NEdK285DJEhdqmirmQxWKFvqmjz8CNyjoDCsqb\nkF/q2AQp5FJEhKjsV6CO7m6EIkL8eFqAk7DRcTHNBiNKe80qXFLVgtLqFrR1mh0eJ5VKoNP6YkRc\nECJ7zTAcGaKCRsVzFoiIxCCTSaELsg3GwHDHdWaLtXskmG2usLLaVpTX2D64Fle1ODy2p8ZHh6kR\nF+6PuAh/xEdooAvy4x6gq8RGRyQWixXltQYUVjShsKIZJVW2xqap1fFkYKlUgohgP2Qk+yNWp0ZM\nuD+iQ1UID1bxvBkiIjcil52fO2ziqPPLrd3TZpR1N0C2Rsh2O3yqGodPnZ881VshQ6xOjbhwDeLC\n/REf4Y+4cH9OPnwJbHQGganLguKqFhRVNKOwohlFFU0ormyByWx1eFyo1hfXjQzrPi6sRmy4PyJD\nVLymAxGRB5P2umL02JQw+/KeyVNLqlpwrrIFxVUtOFfZjKKKZpwpdZwuIzjABwkRGiRFB2BY9417\n923Y6DiZIAio0BuQX9KIvJJG5Jc0oKS61eHEYLlMghidPxIjNUiM1CAhMgCx4WoOTSQiIrvek6dm\nJIfal5u7jwgUVzZ3Nz8tKK5qxg+nq/HD6fN7f0K1vhgWHYDk6AAMiw5EYpRmSP6dYaNzjTqNZuSV\nNCC32NbU5Jc0wtBx/gRhhVyK5OgAJEYFIKG7sYnR+fOwExER9YtcJrWdtxPuOCKssaUTZ8uacKas\nEWfLmnC2tAkHT1Ti4IlKALaLIcbq/JGaEITU+CCMTNAiSOMjxiYMKjY6V8nYZUFecQN+6r6Y1Nmy\nRocZtnVBvhg3IgzDYwMxPDYQ8REajnQiIqIBF+ivxPWpOlyfqgNgO8JQ09Bua3rKmnCmtBFnSxtR\nXNWCfx48BwAID/LDyAQt0hKCkD4sBKGBvmJuwoBgo3MZgiCgvNaAI6dr8GNuDfJKGtDVfW6NVAIk\nRAUgPTEYI+O1GB6rRYCax0SJiEh8EonEPgJsckYkANvFZAvLm3CqqB45RfXILW7AviNl2HekDAAQ\nHaZC5vAwZA4PRWpikEdM48NG5yIsFit+KqzD4VPV+DG3BtX17fZ1CREajEoKRnpSMFITguDnM/SO\ndxIRkXvykkuREqdFSpwWd04fBqtVQEl1C3IK63EsvxY/Fdbh4wOF+PhAIRRyKUYlBWNSegTGp4XD\n3889R3ax0elmtQrILW7AN9kVOHii0j7nk69SjknpEbhuZBjGpoRxjw0REXkMqVSC+AgN4iM0mDs5\nAV1mC04XNeBofi2O5dXgaF4tjubVQvq/J5De3fTcODrCrYazD/lGp7GlE3t+KMGe70tQ29gBwDZf\nyW2T4jFxVDhSE4J4jg0REQ0JXnIZRieHYHRyCO6bm4qqujYcOlmJgycrkX1Gj+wzevxp90+4YXQE\nbh4fi7SEIJefw2vINjpnyxrx4f4CfP9TFSxWAUqFDFnXRWPKmCiMTgqGjM0NERENceHBfrhz+jDc\nOX0Yahra8U12Bb48XIKvjpbjq6PliAxR4Y5pSZg2Nsplp6wYco1OXnED/vZlPo7l1QIA4sL9MfuG\nOEzNjBqS1xcgIiK6EmFaXyyYPgx3TkvCqaJ6fHG4BN9mV2DzB9n46xd5uP2mJMy+Ic7lGp4h0+jo\nGzvw1qencCC7AgAwKjEYi2ckI31YsMvvdiMiInIVEokEaYnBSEsMxsrbRmL314X4/LtivPFxDv75\n7TncOzcVE9J0LvO31eMbHUEQsOdwCf70cQ6MJguSYwJw39w0pCYEiR2NiIjIrQVpfHD/L9KwMCsZ\n73+Zj38ePIeNb/2AiaPC8ciC0S4xDYVHNzodRjNefe8YDp2sgp+PFx5aPArTx8VAyplfiYiInMbf\nT4EH5o/CrTfEYcuuE/jupyrkFjfgP+65DiPjxd2x4LFn3Da1GvHU1m9x6GQVUhOCsPnJaZhxfSyb\nHCIiogESFarGCw9Pwr1zRqKlzYTfbDuEb09UiJrJI/fotHd24Zn/OYTiqhbMvD4Gv1wwmkPEiYiI\nBoFMKsEd04YhPkKDF985gpf/chQKucw+NcVg87i//largE07f0RxVQtunRiH1Ysy2OQQERENsjHD\nQ/HcAxPhJZdi0ztHUFLdIkoOj+sA/vVdMY7l1SIzJRQP3pHuMmd9ExERDTUpcVo8uTQTJrMVv//L\nUftckYOpX4euWltbsW7dOhgMBnR1deHXv/41xowZ4/CY559/HseOHYOfnx8AYOvWrVCr1dee+FK5\n2k14+5+nofLxwuOLx0DG83GI3Iqr1hYi6r+JoyJwy4RYfPF9CfZ8X4zbbkwY1NfvV6OzY8cOTJgw\nAStXrkRRURGefPJJfPTRRw6POXXqFN544w1otVqnBL0SnxwoQofRjPvmpkLrrxy01yUi53DV2kJE\n1+buWSNw4Hg53tt7BjdPiB3Uiwr2q9FZuXIlFArbhF4WiwXe3o7j5K1WK0pKSvDss8+irq4OCxYs\nwIIFC6497SVYrAK++L4YKh8v3DoxbkBfi4gGhivWFiK6dgFqb9w8Pg4fHyjEkdM1uCE9YtBe+7KN\nzq5du/D22287LNu4cSPS09Oh1+uxbt06PPXUUw7r29vbcffdd+Pee++FxWLBihUrkJaWhpSUlJ99\nnc2bN2PLli393Azb1A6NrUbcPD4WSm+PHExG5FEGq7YA115fiOjaZV0XjY8PFOLA8QrXanQWLlyI\nhQsX9lmen5+PtWvXYv369bj++usd1vn4+GDFihXw8fEBAEyYMAF5eXmXLEarV6/G6tWrHZaVl5cj\nKyvrijbkWL5t7qoJaeIMXyOiqzNYtQW49vpCRNcuLtwfIYE+OFmgh8UqDNp5tP0adVVQUIDHH38c\nr7zyCqZOndpnfXFxMe666y5YLBZ0dXXh2LFjSE1Nveawl3KmpBEAMCKOx+2J3JUr1hYicg6JRILU\nhCC0tnehpr5t0F63X8d4XnnlFZhMJrzwwgsAAJVKhW3btmHHjh2IiYlBVlYW5s2bh0WLFsHLywvz\n5s3DsGHDnBr8QueqmqEL8oXKVzGgr0NEA8cVawsROU9MmG2EZFlNKyJCVIPymv1qdLZt23bR5ffe\ne6/9+1WrVmHVqlX9S3WV2ju70GwwITEqYFBej4gGhqvVFiJyrujuRqe0phXj08IH5TU94oKBDS2d\nAIBgjY/ISYiIiOjnhATY/k43thoH7TU9otFpNpgA2IavERERkWvq+TvdzEbn6qh8veAllyIpSiN2\nFCIiIvoZGpU3QgJ9EOA/eDsmPOKCM7E6f7z/wm3wkntE30ZEROSR5DIp/ufXMyCXDd4UTR7R6ABg\nk0NEROQGBvvvtUs3OhaLBQBQXV0tchIi+jk978+e96u7YH0hcm3Oqi0u3ejo9XoAwLJly0ROQkSX\no9frERsbK3aMK8b6QuQerrW2SARBEJyYx6k6OzuRk5ODkJAQyGSXnuk0KysL+/btG6Rk14ZZnc9d\ncgLuk/VKc1osFuj1eqSlpUGpVA5CMudgfRGXu+QE3Ceru+QEriyrs2qLS+/RUSqVGDdu3BU/Pioq\nagDTOBezOp+75ATcJ+uV5nSnPTk9WF/E5y45AffJ6i45gSvL6ozawjN4iYiIyGOx0SEiIiKPxUaH\niIiIPJZsw4YNG8QO4Szjx48XO8IVY1bnc5ecgPtkdZecg8GdfhfuktVdcgLuk9VdcgKDl9WlR10R\nERERXQseuiIiIiKPxUaHiIiIPBYbHSIiIvJYbHSIiIjIY7HRISIiIo/ldo2O1WrFs88+i8WLF2P5\n8uUoKSlxWP/BBx/gjjvuwKJFi7B//36RUl4+51tvvYWFCxdi4cKF2LJli0gpbS6Xtecxq1atwt/+\n9jcREjrmuFTWr7/+GosWLcLChQuxYcMGiDWo8HI5//znP+OOO+7AnXfeiS+//FKUjBc6ceIEli9f\n3mf5v//9b9x5551YvHgxPvjgAxGSDR7WF+dzl/riLrUFcL/6InptEdzMF198IfzqV78SBEEQjh8/\nLjz00EP2dbW1tcKcOXMEo9EotLS02L93tZylpaXC7bffLpjNZsFqtQqLFy8WcnNzRckpCJfO2uOV\nV14RFi5cKPz1r38d7HgOLpW1tbVVuO2224T6+npBEARh+/bt9u9dKWdzc7MwdepUwWg0Ck1NTcJN\nN90kSsbetm/fLsyZM0dYuHChw3KTySTMmDFDaGpqEoxGo3DHHXcIer1epJQDj/XF+dylvrhLbREE\n96ovrlBb3G6PztGjRzF58mQAQEZGBnJycuzrTp48iTFjxkChUECtViMmJgZ5eXkul1On0+GNN96A\nTCaDRCKB2WyGt7e3KDmBS2cFgM8//xwSicT+GDFdKuvx48eRnJyMTZs2YenSpQgODoZWq3W5nD4+\nPoiIiEBHRwc6OjogkUhEydhbTEwMNm/e3Gd5YWEhYmJioNFooFAoMHbsWBw5ckSEhIOD9cX53KW+\nuEttAdyrvrhCbXHp2csvxmAwQKVS2e/LZDKYzWbI5XIYDAao1Wr7Oj8/PxgMBjFiXjKnl5cXtFot\nBEHASy+9hJEjRyI+Pl6UnJfLeubMGXz66af44x//iNdee020jD0ulbWxsRGHDx/G7t274evri2XL\nliEjI0OU3+2lcgJAeHg4brvtNlgsFjz44IODnu9Ct9xyC8rLy/ssd6X31GBgfRncrK5UX9yltlwu\nK+Ba9cUVaovbNToqlQptbW32+1ar1f6Pe+G6trY2h1/kYLpUTgAwGo146qmn4Ofnh9/+9rdiRLS7\nVNbdu3ejpqYG99xzDyoqKuDl5YXIyEhMmTLF5bIGBARg1KhRCAkJAQCMGzcOubm5ohSjS+U8cOAA\namtrsW/fPgDA/fffj8zMTKSnpw96zstxpffUYGB9cT53qS/uUlsul9Vd6stgvp/c7tBVZmYmDhw4\nAADIzs5GcnKyfV16ejqOHj0Ko9GI1tZWFBYWOqx3lZyCIOCXv/wlhg8fjt/97neQyWSiZOxxqazr\n16/Hrl27sHPnTtx+++1YuXKlaE0OcOmsqampOHPmDBoaGmA2m3HixAkkJSW5XE6NRgOlUgmFQgFv\nb2+o1Wq0tLSIkvNyEhMTUVJSgqamJphMJvz4448YM2aM2LEGDOuL87lLfXGX2gJ4Rn0ZzNridnt0\nZs6ciYMHD2LJkiUQBAEbN27Ejh07EBMTg6ysLCxfvhxLly6FIAhYs2aNaMemL5XTarXihx9+gMlk\nwjfffAMAWLt2rWh/QC73O3Ull8v65JNPYtWqVQCAWbNmifaH6HI5Dx06hEWLFkEqlSIzMxOTJk0S\nJefP+cc//oH29nYsXrwYv/71r3H//fdDEATceeedCAsLEzvegGF9GdysrlRf3KW2XElWV64vYtQW\nTupJREREHsvtDl0RERERXSk2OkREROSx2OgQERGRx2KjQ0RERB6LjQ4RERF5LDY6RERE5LHY6BAR\nEZHHYqNDREREHuv/A3t8Mk4+pTOlAAAAAElFTkSuQmCC\n",
      "text/plain": [
       "<matplotlib.figure.Figure at 0x27b410a56a0>"
      ]
     },
     "metadata": {},
     "output_type": "display_data"
    }
   ],
   "source": [
    "''' Different aspects of a normal distribution'''\n",
    "# Generate the data\n",
    "x = r_[-10:10:0.1]\n",
    "rv = stats.norm(0,1)   # random variate\n",
    "\n",
    "x2 = r_[0:1:0.001]\n",
    "\n",
    "ax = subplot2grid((3,2),(0,0), colspan=2)\n",
    "plot(x,rv.pdf(x))\n",
    "xlim([-10,10])\n",
    "title('Normal Distribution - PDF')\n",
    "\n",
    "subplot(323)\n",
    "plot(x,rv.cdf(x))\n",
    "xlim([-4,4])\n",
    "title('CDF: cumulative distribution fct')\n",
    "\n",
    "subplot(324)\n",
    "plot(x,rv.sf(x))\n",
    "xlim([-4,4])\n",
    "title('SF: survival fct')\n",
    "\n",
    "subplot(325)\n",
    "plot(x2,rv.ppf(x2))\n",
    "title('PPF')\n",
    "\n",
    "subplot(326)\n",
    "plot(x2,rv.isf(x2))\n",
    "title('ISF')\n",
    "tight_layout()\n",
    "show()\n",
    "    "
   ]
  },
  {
   "cell_type": "markdown",
   "metadata": {
    "slideshow": {
     "slide_type": "subslide"
    }
   },
   "source": [
    "## Shifted distribution"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 3,
   "metadata": {
    "collapsed": false,
    "slideshow": {
     "slide_type": "fragment"
    }
   },
   "outputs": [
    {
     "data": {
      "text/plain": [
       "<matplotlib.text.Text at 0x27b4256e198>"
      ]
     },
     "execution_count": 3,
     "metadata": {},
     "output_type": "execute_result"
    },
    {
     "data": {
      "image/png": "iVBORw0KGgoAAAANSUhEUgAAAfgAAAFdCAYAAADv+X8iAAAABHNCSVQICAgIfAhkiAAAAAlwSFlz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\nrkJggg==\n",
      "text/plain": [
       "<matplotlib.figure.Figure at 0x27b41087cc0>"
      ]
     },
     "metadata": {},
     "output_type": "display_data"
    }
   ],
   "source": [
    "'''PDF, scatter plot, and histogram.'''\n",
    "# Generate the data\n",
    "x = arange(-5,15,0.1)\n",
    "# Plot a normal distribution: \"Probability density functions\"\n",
    "myMean = 5\n",
    "mySD = 2\n",
    "y = normpdf(x, myMean, mySD)\n",
    "# or: y = stats.norm.pdf(x, myMean, mySD)\n",
    "plot(x,y)\n",
    "title('Shifted Normal Distribution')"
   ]
  },
  {
   "cell_type": "markdown",
   "metadata": {
    "slideshow": {
     "slide_type": "subslide"
    }
   },
   "source": [
    "## Random numbers with a normal distribution"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 4,
   "metadata": {
    "collapsed": false,
    "slideshow": {
     "slide_type": "fragment"
    }
   },
   "outputs": [
    {
     "data": {
      "image/png": "iVBORw0KGgoAAAANSUhEUgAAAeoAAAFdCAYAAADMoi73AAAABHNCSVQICAgIfAhkiAAAAAlwSFlz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\n",
      "text/plain": [
       "<matplotlib.figure.Figure at 0x27b425c64a8>"
      ]
     },
     "metadata": {},
     "output_type": "display_data"
    },
    {
     "data": {
      "text/plain": [
       "<matplotlib.text.Text at 0x27b4268f6d8>"
      ]
     },
     "execution_count": 4,
     "metadata": {},
     "output_type": "execute_result"
    },
    {
     "data": {
      "image/png": "iVBORw0KGgoAAAANSUhEUgAAAfAAAAFdCAYAAAD8Lj/WAAAABHNCSVQICAgIfAhkiAAAAAlwSFlz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\nRK5CYII=\n",
      "text/plain": [
       "<matplotlib.figure.Figure at 0x27b4265de10>"
      ]
     },
     "metadata": {},
     "output_type": "display_data"
    }
   ],
   "source": [
    "numData = 500\n",
    "data = stats.norm.rvs(myMean, mySD, size = numData)\n",
    "plot(data, '.')\n",
    "title('Normally distributed data')\n",
    "show()\n",
    "\n",
    "hist(data)\n",
    "title('Histogram of normally distributed data')"
   ]
  },
  {
   "cell_type": "markdown",
   "metadata": {
    "slideshow": {
     "slide_type": "subslide"
    }
   },
   "source": [
    "## Multiple normal sample distributions"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 5,
   "metadata": {
    "collapsed": false,
    "slideshow": {
     "slide_type": "fragment"
    }
   },
   "outputs": [
    {
     "data": {
      "image/png": "iVBORw0KGgoAAAANSUhEUgAAAjQAAAGACAYAAAC6OPj9AAAABHNCSVQICAgIfAhkiAAAAAlwSFlz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\nAAjvPyorVXQIXrCiAAAAAElFTkSuQmCC\n",
      "text/plain": [
       "<matplotlib.figure.Figure at 0x27b426c7cc0>"
      ]
     },
     "metadata": {},
     "output_type": "display_data"
    },
    {
     "name": "stdout",
     "output_type": "stream",
     "text": [
      "The standard error of the mean, with 100 samples, is 0.200\n"
     ]
    }
   ],
   "source": [
    "'''Show multiple samples from the same distribution, and compare means.'''\n",
    "# Do this 25 times, and show the histograms\n",
    "numRows = 5\n",
    "numData = 100\n",
    "for ii in range(numRows):\n",
    "    for jj in range(numRows):\n",
    "        data = stats.norm.rvs(myMean, mySD, size=numData)\n",
    "        subplot(numRows,numRows,numRows*ii+jj+1)\n",
    "        hist(data)\n",
    "\n",
    "        xticks([])\n",
    "        yticks([])\n",
    "        xlim(myMean-3*mySD, myMean+3*mySD)\n",
    "\n",
    "tight_layout()\n",
    "show()\n",
    "\n",
    "# Check out the mean of 1000 normally distributded samples\n",
    "numTrials = 1000;\n",
    "numData = 100\n",
    "myMeans = ones(numTrials)*nan\n",
    "for ii in range(numTrials):\n",
    "    data = stats.norm.rvs(myMean, mySD, size=numData)\n",
    "    myMeans[ii] = mean(data)\n",
    "print('The standard error of the mean, with {0} samples, is {1:5.3f}'.format(numData, std(myMeans)))"
   ]
  },
  {
   "cell_type": "markdown",
   "metadata": {
    "slideshow": {
     "slide_type": "slide"
    }
   },
   "source": [
    "## Normality Check"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 6,
   "metadata": {
    "collapsed": false,
    "slideshow": {
     "slide_type": "fragment"
    }
   },
   "outputs": [
    {
     "data": {
      "text/plain": [
       "(array([  4.,   6.,  10.,   7.,  19.,  15.,  15.,  15.,   5.,   4.]),\n",
       " array([ 0.76711319,  1.55863157,  2.35014995,  3.14166832,  3.9331867 ,\n",
       "         4.72470508,  5.51622346,  6.30774183,  7.09926021,  7.89077859,\n",
       "         8.68229697]),\n",
       " <a list of 10 Patch objects>)"
      ]
     },
     "execution_count": 6,
     "metadata": {},
     "output_type": "execute_result"
    },
    {
     "data": {
      "image/png": "iVBORw0KGgoAAAANSUhEUgAAAfMAAAFVCAYAAAD7Sga4AAAABHNCSVQICAgIfAhkiAAAAAlwSFlz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\nmAMAYBwxBwDAOGIOAIBxxBwAAOP+D9s9KqfVf1sSAAAAAElFTkSuQmCC\n",
      "text/plain": [
       "<matplotlib.figure.Figure at 0x27b426670f0>"
      ]
     },
     "metadata": {},
     "output_type": "display_data"
    }
   ],
   "source": [
    "'''Check if the distribution is normal.'''\n",
    "# Generate and show a distribution\n",
    "numData = 100\n",
    "data = stats.norm.rvs(myMean, mySD, size=numData)\n",
    "hist(data)"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 7,
   "metadata": {
    "collapsed": false,
    "slideshow": {
     "slide_type": "subslide"
    }
   },
   "outputs": [
    {
     "data": {
      "image/png": "iVBORw0KGgoAAAANSUhEUgAAAfMAAAFrCAYAAADFOmBlAAAABHNCSVQICAgIfAhkiAAAAAlwSFlz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\nEREH9//wfL8vycmIbAAAAABJRU5ErkJggg==\n",
      "text/plain": [
       "<matplotlib.figure.Figure at 0x27b41087b38>"
      ]
     },
     "metadata": {},
     "output_type": "display_data"
    }
   ],
   "source": [
    "# Graphical test: if the data lie on a line, they are pretty much\n",
    "# normally distributed\n",
    "_ = stats.probplot(data, plot=plt)"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 8,
   "metadata": {
    "collapsed": false,
    "slideshow": {
     "slide_type": "subslide"
    }
   },
   "outputs": [
    {
     "name": "stdout",
     "output_type": "stream",
     "text": [
      "Data are probably normally distributed\n"
     ]
    }
   ],
   "source": [
    "# The scipy \"normaltest\" is based on D’Agostino and Pearson’s test that\n",
    "# combines skew and kurtosis to produce an omnibus test of normality.\n",
    "_, pVal = stats.normaltest(data)\n",
    "\n",
    "# Or you can check for normality with Kolmogorov-Smirnov test: but this is only advisable for large sample numbers!\n",
    "#_,pVal = stats.kstest((data-np.mean(data))/np.std(data,ddof=1), 'norm')\n",
    "\n",
    "if pVal > 0.05:\n",
    "    print('Data are probably normally distributed')\n"
   ]
  },
  {
   "cell_type": "markdown",
   "metadata": {
    "slideshow": {
     "slide_type": "slide"
    }
   },
   "source": [
    "## Values from the Cumulative Distribution Function"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 10,
   "metadata": {
    "collapsed": false,
    "slideshow": {
     "slide_type": "fragment"
    }
   },
   "outputs": [
    {
     "name": "stdout",
     "output_type": "stream",
     "text": [
      "With a threshold of 2.00, you get 7.0% of the data\n",
      "To get 97.5% of the data, you need a threshold of 8.92.\n"
     ]
    },
    {
     "data": {
      "image/png": "iVBORw0KGgoAAAANSUhEUgAAAe0AAAFVCAYAAADCLbfjAAAABHNCSVQICAgIfAhkiAAAAAlwSFlz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\n",
      "text/plain": [
       "<matplotlib.figure.Figure at 0x27b4391bbe0>"
      ]
     },
     "metadata": {},
     "output_type": "display_data"
    }
   ],
   "source": [
    "'''Calculate an empirical cumulative distribution function, compare it with the exact one, and\n",
    "find the exact point for a specific data value.'''\n",
    "\n",
    "# Generate normally distributed random data\n",
    "myMean = 5\n",
    "mySD = 2\n",
    "numData = 100\n",
    "data = stats.norm.rvs(myMean, mySD, size=numData)\n",
    "\n",
    "# Calculate the cumulative distribution function, CDF\n",
    "numbins = 20\n",
    "counts, bin_edges = histogram(data, bins=numbins, normed=True)\n",
    "cdf = cumsum(counts)\n",
    "cdf /= max(cdf)\n",
    "\n",
    "# compare with the exact CDF\n",
    "step(bin_edges[1:],cdf)\n",
    "plot(x, stats.norm.cdf(x, myMean, mySD),'r')\n",
    "\n",
    "# Find out the value corresponding to the x-th percentile: the\n",
    "# \"cumulative distribution function\"\n",
    "value = 2\n",
    "myMean = 5\n",
    "mySD = 2\n",
    "cdf = stats.norm.cdf(value, myMean, mySD)\n",
    "print(('With a threshold of {0:4.2f}, you get {1}% of the data'.format(value, round(cdf*100))))\n",
    "\n",
    "# For the percentile corresponding to a certain value: \n",
    "# the \"inverse cumulative distribution function\" \n",
    "value = 0.025\n",
    "icdf = stats.norm.isf(value, myMean, mySD)\n",
    "print('To get {0}% of the data, you need a threshold of {1:4.2f}.'.format((1-value)*100, icdf))"
   ]
  }
 ],
 "metadata": {
  "celltoolbar": "Slideshow",
  "kernelspec": {
   "display_name": "Python 3",
   "language": "python",
   "name": "python3"
  },
  "language_info": {
   "codemirror_mode": {
    "name": "ipython",
    "version": 3
   },
   "file_extension": ".py",
   "mimetype": "text/x-python",
   "name": "python",
   "nbconvert_exporter": "python",
   "pygments_lexer": "ipython3",
   "version": "3.6.0"
  }
 },
 "nbformat": 4,
 "nbformat_minor": 0
}
